CN104301578A - Image type-based processing method and device - Google Patents

Abstract

The invention discloses a processing method and equipment based on an image type and relates to the technical field of image processing. The equipment comprises a judging module, a compression quality factor determining module, a first scaling module and a sharpening module, wherein the judging module is suitable for judging the type of an input image; the compression quality factor determining module is suitable for obtaining compression quality factors indicating the compression ratio of the colorful image under the situation that the judging module judges that the input image is a colorful image; the first scaling module is suitable for scaling the colorful image; the sharpening module is suitable for sharpening the colorful image obtained after scaling according to the compression quality factors of the colorful image and obtaining the output colorful image. The method and the equipment disclosed by the invention have the benefits that the image is sharpened according to the compression ratio of the colorful image, and the definition of an original low-quality color image is greatly improved after sharpening.

Description

Image type-based processing method and device

This application is a divisional application of an invention patent application with a filing date of October 19, 2012, an application number of 201210402441.5, and a title of "Image Type-based Processing Method and Equipment".

technical field

The present invention relates to the technical field of image processing, in particular to a processing method and device based on image type.

Background technique

In computer image processing, image scaling is the process of resizing a digital image by adding or removing pixels. Image scaling is not a trivial process due to the trade-off between efficiency and image quality such as smoothness and sharpness. At present, there are many tools for image processing on the client side, such as Photoshop and Meitu Xiuxiu, etc. Through these tools, the image can be zoomed according to the user's preference.

The image scaling technology includes two technologies of image reduction and image enlargement. Image enlargement is generally used to fill a larger screen with a smaller image. When the size of the image increases, the pixels that make up the image increase, and the image looks "softer". In addition to shrinking the image to fit the display area, image reduction is more used to generate a preview image. Image enlargement technology can generally be realized by interpolation algorithm. The image reduction technology can be implemented by sampling (also called down-sampling or down-sampling) method. The zoom operation on the picture cannot bring more information about the picture, so the quality of the picture will inevitably be affected after zooming. Taking the reduction of JPEG (Joint Photographic Experts Group, Joint Photographic Experts Group) picture as an example, some pixels in the picture will be removed by sampling, which will inevitably cause problems such as blurred and grainy pictures. If the quality of the JPEG picture is worse, The resulting thumbnails are blurrier.

Contents of the invention

In view of the above problems, the present invention is proposed to provide a picture-type-based processing method and a corresponding picture-type-based processing device that overcome the above-mentioned problems or at least partially solve the above-mentioned problems.

According to one aspect of the present invention, a processing method based on picture type is provided, comprising the steps of: judging the type of the input picture; in the case of judging that the type of the input picture is a color picture, obtaining the compression ratio indicating the color picture The compression quality factor of the color picture is scaled; and according to the compression quality factor of the color picture, image sharpening is performed on the color picture obtained after scaling to obtain an output color picture.

According to another aspect of the present invention, a processing device based on picture type is provided, including: a judging module, adapted to judge the type of an input picture; a compression quality factor determining module, adapted to judge the input In the case that the type of the picture is a color picture, the compression quality factor indicating the compression rate of the color picture is obtained; the first scaling module is adapted to perform scaling processing on the color picture; and the sharpening module is adapted to compress the quality factor according to the color picture , performing image sharpening processing on the color image obtained after scaling processing to obtain an output color image.

According to the picture type-based processing method and equipment provided by the present invention, by judging the type of the input picture, if it is judged to be a color picture, the compression quality factor of the color picture is obtained, and the compression quality factor of the color picture is compared The image sharpening process is performed on the zoomed color picture, wherein the compression quality factor indicates the compression ratio of the color picture, that is to say, the method and device provided by the present invention perform image sharpening processing according to the compression rate of the color picture, after such After processing, the clarity of the original low-quality color pictures has been greatly improved.

The above description is only an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention, it can be implemented according to the contents of the description, and in order to make the above and other purposes, features and advantages of the present invention more obvious and understandable , the specific embodiments of the present invention are enumerated below.

Description of drawings

Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiment. The drawings are only for the purpose of illustrating a preferred embodiment and are not to be considered as limiting the invention. Also throughout the drawings, the same reference numerals are used to designate the same components. In the attached picture:

FIG. 1 shows a flowchart of a processing method based on a picture type according to an embodiment of the present invention;

FIG. 2 shows a flowchart of a processing method based on a picture type according to another embodiment of the present invention; and

Fig. 3 shows a schematic structural diagram of a processing device based on a picture type according to an embodiment of the present invention.

Detailed ways

Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided for more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

Fig. 1 shows a flowchart of a processing method 100 based on a picture type according to an embodiment of the present invention. The method provided in this embodiment mainly solves how to perform scaling processing on the color picture when it is determined that the type of the input picture is a color picture. As shown in FIG. 1 , the method 100 starts at step S101 , where the type of the input picture is determined. Due to the different types of input pictures, subsequent processing after zooming will be different. Therefore, in this method, it is first necessary to judge the type of the input picture. The judgment process is mainly to determine whether the input picture is a color picture. Optionally, the judgment is based on the color space of the input picture, and if the color space of the input picture is RGB, then it is determined that the type of the input picture is a color picture.

Subsequently, the method 100 proceeds to step S102, and if it is determined in step S101 that the type of the input picture is a color picture, in step S102, the compression quality factor indicating the compression rate of the color picture is obtained. Currently, color images on the Internet are compressed to varying degrees. Taking JPEG images as an example, the compression of this type of image mainly includes four steps, namely color conversion, DCT transform (Discrete Cosine Transform, discrete cosine transform), quantization and encoding. A compression quality factor is chosen in the quantization step, which indicates the compression ratio of the color picture. If the selected compression quality factor is large, the compression ratio can be greatly improved, but the image quality will be poor; if the selected compression quality factor is small, the reconstruction quality of the image is better, but the compression ratio is low. Therefore, the compression quality factor is a factor that characterizes the image quality of a color picture. If it is determined that the type of the input picture is a color picture, the compression quality factor of the color picture is obtained, and the compression quality factor will be used in subsequent steps of the method.

The method 100 then proceeds to step S103, where scaling processing is performed on the color picture. After obtaining the compression quality factor of the color picture, the color picture is scaled according to an interpolation or sampling algorithm. The scaling processing in this step can be performed according to an existing method, which will not be repeated here.

It should be noted that there is no sequence relationship between steps S102 and S103, and the two steps can be executed simultaneously, step S103 first, then step S102, etc., all of which are within the protection scope of the present invention.

Subsequently, in step S104, according to the compression quality factor of the color picture obtained in step S102, the image sharpening process is performed on the color picture obtained after scaling processing to obtain an output color picture. The image sharpening processing performed in step S104 is to compensate and increase the high-frequency components of the image, so as to make the object boundaries, region edges, lines, texture features and fine structure features in the image more clear and distinct. Since the noise part of the picture will be enhanced to the same extent during the image sharpening process, and for a picture with a higher compression rate, the pixel loss is more serious and the noise is correspondingly larger, so this method has different effects Color images with compressed quality factors undergo varying degrees of image sharpening. Specifically, the higher the compression ratio indicated by the compression quality factor, the lower the sharpness of image sharpening, and the lower the compression ratio indicated by the compression quality factor, the higher the sharpness of image sharpening. After such image sharpening processing, the clarity of the obtained output color picture is greatly improved.

According to the method for zooming a picture provided in this embodiment, by judging the type of the input picture, if it is judged to be a color picture, the compression quality factor of the color picture is obtained, and the compression quality factor of the color picture is compared to The image sharpening process is performed on the scaled color picture, wherein the compression quality factor indicates the compression ratio of the color picture, that is to say, the method provided in this embodiment performs image sharpening processing according to the compression rate of the color picture, after such processing After that, the sharpness of the original low-quality color pictures has been greatly improved.

Fig. 2 shows a flowchart of a processing method 200 based on a picture type according to another embodiment of the present invention. As shown in FIG. 2 , the method 200 starts at step S201 , where the type of the input picture is determined. Due to the different types of input pictures, the subsequent processing after zooming will be different. Therefore, in this method, the type of the input picture needs to be judged first. The judgment process is mainly to determine whether the input picture is a color picture or a grayscale picture. Optionally, the basis for judging is the color space of the input picture, if the color space of the input picture is grayscale, then it is determined that the type of the input picture is a grayscale picture, and step S202 is performed; if the color space of the input picture is RGB, Then it is determined that the type of the input picture is a color picture, and step S204 is executed.

If it is determined in step S201 that the type of the input image is a grayscale image, the method 200 proceeds to step S202, where the grayscale image is scaled. According to the interpolation or sampling algorithm, the grayscale image is scaled. The scaling processing in this step can be performed according to an existing method, which will not be repeated here.

After scaling the grayscale image in step S202, the method 200 proceeds to step S203, wherein the grayscale image obtained after scaling is subjected to histogram equalization processing to obtain an output grayscale image. For the gray picture, this method adopts the histogram equalization method to process it, which can enhance the contrast of the picture. Histogram equalization is a method in the field of image processing that uses image histograms to adjust image contrast. The abscissa of the image histogram represents the brightness distribution, and the ordinate represents the pixel distribution. It shows the distribution of tones in an image, revealing how many pixels are present at each brightness level in the image. According to the shape of the image drawn by these values, the exposure of the image can be preliminarily judged. Whether the picture has rich highlights or is overexposed, or has full details and dark tones or details are unclear, the image histogram can be displayed intuitively. The histogram equalization method achieves contrast enhancement by "adjusting" the gray value using a cumulative function. The "central idea" of histogram equalization processing is to change the gray histogram of the original image from a relatively concentrated gray-scale interval to a uniform distribution in the entire gray-scale range. Histogram equalization is to stretch the image nonlinearly and redistribute the pixel values of the image so that the number of pixels in a certain gray scale range is roughly the same. Simply put, it is to change the histogram distribution of a given image into a histogram distribution of "uniform" distribution.

For a grayscale image, let n _i represent the number of occurrences of grayscale i, and the probability of occurrence of a pixel with grayscale i in the image is:

${\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; x</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; no</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; \&Element;</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; L</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>$

Among them, L is all the grayscale numbers in the picture, n is the number of all pixels in the picture, and p _x is actually the histogram of the picture, which is normalized to the range [0,1].

Taking c as the cumulative probability function corresponding to p _x , it is defined as:

$\mathrm{<span\; class="notranslate">\; c</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}}{\overset{\mathrm{<span\; class="notranslate">\; i</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; x</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>$

c is the cumulative normalized histogram of the image.

Create a variation function of the form y=T(x), which produces a y for each value in the original image, so that the cumulative probability function of y can be linearized over all values, and the transformation formula is defined as:

y _i =T(x _i )=c(i) (3)

Note that T maps different levels to the [0,1] domain, to map these values back to their original domain, the following simple transformation needs to be applied on the result:

y′ _i =y _i ·(max-min)+min (4)

Among them, max represents the maximum gray value in the picture, and min represents the minimum gray value in the picture.

Since the pixels of the grayscale image are lost after zooming and become blurred, the above-mentioned histogram equalization method can make the pixels of the image tend to occupy the entire possible grayscale and be evenly distributed, so as to enhance the contrast between the foreground and background of the image The purpose of making the output grayscale image clearer.

The method using histogram equalization on grayscale images was described above, but it can also be done on color images by applying this method separately to the red, green and blue components of the image's RGB color values.

If it is determined in step S201 that the type of the input picture is a color picture, the method 200 proceeds to step S204, and in step S204, the compression quality factor indicating the compression rate of the color picture is acquired. Currently, color images on the Internet are compressed to varying degrees. In order to better understand the source and function of the compression quality factor, here is a JPEG image as an example to introduce the process of image compression. JPEG image compression is mainly divided into the following four steps to complete:

(1) Color conversion.

Since JPEG only supports the data structure of the YUV color mode, but not the data structure of the RGB color mode, before compressing the color picture, the color mode of the color picture must be converted to the RGB color mode.

(2) DCT transformation.

The image signal is transformed in the frequency domain to separate high-frequency information and low-frequency information. Subsequently, the high-frequency part of the image (ie image details) will be compressed to achieve the purpose of compressing image data.

(3) Quantification.

Since the codebooks used in the subsequent encoding process are all integers, it is necessary to quantize the frequency coefficients after DCT transformation and convert them into integers. During this process, a compression quality factor is selected, which indicates the compression ratio of the color picture. After the quantization process is performed according to the compression quality factor, the quantized values obtained are all approximate values, which are different from the data of the original image, and this difference is the main reason for image distortion after compression. Therefore, the selection of the compression quality factor is very important. If the selected compression quality factor is large, the compression ratio can be greatly improved, but the image quality will be poor; if the selected compression quality factor is small, the reconstruction quality of the image is better. But the compression is relatively low. ISO has developed a set of standard quantization values for use by implementers of JPEG codes.

(4) Coding.

It can be seen from the previous process that the image has not been further compressed from the completion of color conversion to the encoding, and DCT transformation and quantization can be said to be preparations for the encoding stage. Encoding can use two mechanisms: one is 0-value run-length encoding; the other is entropy encoding. In JPEG, a zigzag sequence is adopted, that is, the elements in the image data matrix are arranged in a zigzag with the normal direction of the diagonal of the image data matrix. The advantage of doing this is that the elements near the upper left corner of the image data matrix with relatively large values are arranged in front of the run, and the elements of the image data matrix arranged behind the run are basically 0 values. Run-length encoding is a very simple and commonly used encoding method, which will not be repeated here. Coding is actually a coding method based on statistical properties. Huffman coding or arithmetic coding is allowed in JPEG.

Through the compression process of the above JPEG image, we can see that the compression quality factor can be flexibly controlled, and different implementations have different definitions for the compression quality factor, for example:

IJG (Independent JPEG Group, Independent JPEG Group) uses a compression quality factor measurement level of 1 to 99, and 1 indicates the lowest compression rate of the picture, and 99 indicates the highest compression rate of the picture;

Photoshop (an image processing software) defines a compression quality factor of 1 to 12;

Apple (Apple) defines the compression quality factor from 0 to 4;

Paint Shop Pro (an image editing software) uses a compression quality factor scale of 1 to 99, but contrary to IJG, 1 indicates the highest compression rate of the picture, and 99 indicates the lowest compression rate of the picture.

For the above-mentioned several differently defined compression quality factors, they can be transformed into a unified measurement level. For example, after analyzing the content of the color picture to obtain the compression quality factor of the color picture, it can be converted into a measurement level defined by IJG. Optionally, this function can be realized by using the JPEGdump tool. It should be noted that the present invention is not limited to transforming the compression quality factor into a metric level defined by the IJG, and may also be converted into a metric level defined by other types, which is not limited by the present invention.

The method 200 then proceeds to step S205, wherein the format of the color picture is converted from the original format to PNG format (Portable Network Graphic Format, streaming network graphic format). Because the picture processing process of PNG format is a lossless process, this method selects the format (such as JPEG format) of color picture to be converted into PNG format before scaling process, with respect to other formats, the color picture of PNG format is carried out scaling process, The impact on clarity can be reduced.

Subsequently, in step S206, scaling processing is performed on the color picture. Specifically, scaling processing is performed on the color picture according to an interpolation or sampling algorithm. The zooming process in step S206 can be performed according to an existing method, which will not be repeated here.

After scaling the color picture in step S206, the method 200 proceeds to step S207, wherein the format of the scaled color picture is converted from the PNG format to the original format. In order to obtain a smaller number of bytes, the scaled color picture is converted to the original format (such as JPEG format).

The above step S205 and step S207 are optional steps of the present invention, and the present invention can also directly perform scaling processing on the color picture in the original format without format conversion. However, in the case of format conversion, the clarity of the obtained color pictures will be better.

It should be noted that there is no sequence relationship between step S204 and any of steps S205-S207, and step S204 can be executed simultaneously with any of steps S205-S207, or can be performed during any of steps S205-S207. steps are performed afterwards, all of which are within the scope of the present invention.

Subsequently, in step S208, convolution processing is performed on the color picture obtained after the scaling process and Gaussian signals indicating different sharpening degrees to obtain an output color picture. This method achieves the purpose of image sharpening by convolving the color picture with the Gaussian signal, where the sharpness indicated by the Gaussian signal is determined by the parameters in the Gaussian signal, and the size of the parameters in the Gaussian signal is Determined according to the compression quality factor of the color picture obtained in step S204. Let the color picture be f(x, y) and the Gaussian signal be g(r, σ), then the output color picture after convolution processing is:

R(x,y)=f(x,y)*g(r,σ) (5)

Among them, r and σ are parameters in the Gaussian signal, r is the mean value of the Gaussian signal, and σ is the variance of the Gaussian signal. The value of r and σ is determined according to the compression quality factor of the color picture.

Take the compression quality factor Q of the metric level defined by IJG as an example:

If Q≥95, r=5, σ=0.4;

If 90≤Q<95, r=5, σ=0.3;

If 85≤Q<90, r=5, σ=0.2;

If Q<85, no sharpening is performed.

The specific values of the parameters of the above-mentioned Gaussian signal are one of the embodiments provided by the present invention, but should not be regarded as limitations of the present invention.

According to the method for zooming a picture provided in this embodiment, by judging the type of the input picture, if it is judged to be a grayscale picture, perform histogram equalization processing on the scaled grayscale picture, so that The pixels of a grayscale image tend to occupy the entire possible grayscale and are evenly distributed, thereby achieving the purpose of enhancing the contrast between the foreground and background of the image, and making the output grayscale image clearer. If it is judged to be a color picture, the compression quality factor of the color picture is acquired, and image sharpening is performed on the scaled color picture according to the compression quality factor of the color picture, wherein the compression quality factor indicates the compression quality of the color picture That is to say, the method provided in this embodiment performs image sharpening processing on the color image to different degrees according to the compression ratio of the color image. After such processing, the definition of the original low-quality image is greatly improved. In a word, the scaling process of any type of pictures can be optimized by using the method provided in this embodiment, so that the scaled pictures are clearer. This method is especially suitable for enterprise-level batch processing of pictures.

Fig. 3 shows a schematic structural diagram of a processing device based on a picture type according to an embodiment of the present invention. As shown in FIG. 3 , the image scaling device 300 includes a judging module 310 , a compression quality factor determining module 320 , a first scaling module 330 and a sharpening module 340 .

In the picture zooming device 300, the judging module 310 receives the input picture sent from the outside, and judges the type of the input picture. Due to the different types of input pictures, the processing done after the pictures are zoomed will be different. Therefore, in this device, the judging module 310 needs to first judge the type of the input pictures. This judgment process mainly determines whether the input pictures are color pictures or gray pictures. degree picture. Optionally, the basis for judging is the color space of the input picture, if the color space of the input picture is grayscale, then determine that the type of the input picture is a grayscale picture; if the color space of the input picture is RGB, then determine the The type of the input image is a color image.

The compression quality factor determination module 320 acquires the compression quality factor indicating the compression rate of the color picture when the judging module 310 determines that the type of the input picture is a color picture. Currently, color pictures on the Internet are compressed to varying degrees. Taking JPEG pictures as an example, the compression of this type of picture mainly includes four steps, namely color conversion, DCT transformation, quantization and encoding. The details of these four steps For the process, reference may be made to the description in the foregoing method embodiments. A compression quality factor is chosen in the quantization step, which indicates the compression ratio of the color picture. If the selected compression quality factor is large, the compression ratio can be greatly improved, but the image quality will be poor; if the selected compression quality factor is small, the reconstruction quality of the image is better, but the compression ratio is low. Therefore, the compression quality factor is a factor that characterizes the image quality of a color picture. If it is determined that the type of the input picture is a color picture, the compression quality factor determination module 320 acquires the compression quality factor of the color picture. Optionally, the compression quality factor determining module 320 is specifically adapted to obtain the compression quality factor of the color picture by analyzing the content of the color picture, and convert the compression quality factor into an IJG metric level, for example, this function can be realized by using a JPEGdump tool. Afterwards, the compression quality factor determination module 320 outputs the compression quality factor to the sharpening module 340 .

The first scaling module 330 scales the color picture. Specifically, the first scaling module 330 scales the color picture according to an interpolation or sampling algorithm.

The sharpening module 340 performs image sharpening processing on the color picture obtained after scaling according to the compression quality factor of the color picture to obtain an output color picture. Wherein, the higher the compression rate indicated by the compression quality factor, the lower the sharpening degree of image sharpening performed by the sharpening module 340 .

Optionally, the sharpening module 340 may include: a sharpening processing module 341 and a parameter acquisition module 342, wherein the sharpening processing module 341 performs convolution processing on the color picture obtained after scaling processing and Gaussian signals indicating different sharpening degrees , obtain and output the output color picture; the parameter acquisition module 342 determines the size of the parameter in the Gaussian signal according to the compression quality factor of the color picture, and the sharpness indicated by the Gaussian signal is determined by the parameter in the Gaussian signal. For the detailed process of sharpening the color picture by the sharpening processing module 341 , please refer to the related description in the above method embodiment.

Optionally, the picture zooming device 300 may also include a format conversion module 350, which is suitable for converting the format of the color picture from the original format to the PNG format, and converting the format of the color picture obtained after scaling from PNG to PNG. The format is converted to the original format. Since the image processing process in PNG format is a lossless process, this device chooses to convert the format of the color image (such as JPEG format) to PNG format before scaling. Compared with other formats, the color image in PNG format is scaled. The impact on clarity can be reduced.

Further, the image scaling device 300 may further include a second scaling module 360 and a histogram equalization processing module 370 . Wherein, the second scaling module 360 performs scaling processing on the grayscale picture when the judging module 310 determines that the type of the input picture is a grayscale picture. The second scaling module 360 can perform scaling processing on the gray scale image according to an interpolation or sampling algorithm. The histogram equalization processing module 370 performs histogram equalization processing on the grayscale image obtained after scaling processing, and obtains and outputs an output grayscale image. For the gray picture, the device adopts histogram equalization method to process it, which can enhance the contrast of the picture. For the detailed process of the histogram equalization processing module 370 performing the histogram equalization processing on the grayscale picture, refer to the related description of the above method embodiment.

According to the device for zooming the image provided in this embodiment, the type of the input image is judged by the judging module. Perform histogram equalization processing, so that the pixels of the grayscale image tend to occupy the entire possible grayscale and distribute evenly, thereby achieving the purpose of enhancing the contrast between the foreground and background of the image, and making the output grayscale image clearer. In the case of judging that it is a color picture, the compression quality factor determination module obtains the compression quality factor of the color picture, and the sharpening module performs image sharpening processing on the scaled color picture according to the compression quality factor of the color picture, wherein the compression quality factor The quality factor indicates the compression rate of the color picture. That is to say, the device provided in this embodiment performs image sharpening processing on the color picture in different degrees according to the compression rate of the color picture. After such processing, the original low-quality The sharpness of the picture has been greatly improved. In a word, the scaling process of any type of picture can be optimized by using the device provided in this embodiment, so that the scaled picture is clearer, and the device is especially suitable for enterprise-level batch processing of pictures.

The algorithms and displays presented herein are not inherently related to any particular computer, virtual system, or other device. Various generic systems can also be used with the teachings based on this. The structure required to construct such a system is apparent from the above description. Furthermore, the present invention is not specific to any particular programming language. It should be understood that various programming languages can be used to implement the content of the present invention described herein, and the above description of specific languages is for disclosing the best mode of the present invention.

In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure the understanding of this description.

Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, in order to streamline this disclosure and to facilitate an understanding of one or more of the various inventive aspects, various features of the invention are sometimes grouped together in a single embodiment, figure, or its description. This method of disclosure, however, is not to be interpreted as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the Detailed Description are hereby expressly incorporated into this Detailed Description, with each claim standing on its own as a separate embodiment of this invention.

Those skilled in the art can understand that the modules in the device in the embodiment can be adaptively changed and arranged in one or more devices different from the embodiment. Modules or units or components in the embodiments may be combined into one module or unit or component, and furthermore may be divided into a plurality of sub-modules or sub-units or sub-assemblies. All features disclosed in this specification (including accompanying claims, abstract and drawings) and any method or method so disclosed may be used in any combination, except that at least some of such features and/or processes or units are mutually exclusive. All processes or units of equipment are combined. Each feature disclosed in this specification (including accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.

Furthermore, those skilled in the art will understand that although some embodiments described herein include some features included in other embodiments but not others, combinations of features from different embodiments are meant to be within the scope of the invention. and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.

The various component embodiments of the present invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art should understand that a microprocessor or a digital signal processor (DSP) can be used in practice to implement some or all functions of some or all of the components in the device for zooming pictures according to the embodiment of the present invention . The present invention can also be implemented as an apparatus or an apparatus program (for example, a computer program and a computer program product) for performing a part or all of the methods described herein. Such a program for realizing the present invention may be stored on a computer-readable medium, or may be in the form of one or more signals. Such a signal may be downloaded from an Internet site, or provided on a carrier signal, or provided in any other form.

It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention can be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In a unit claim enumerating several means, several of these means can be embodied by one and the same item of hardware. The use of the words first, second, and third, etc. does not indicate any order. These words can be interpreted as names.

The invention discloses: A1, a processing method based on picture type, comprising:

Determine the type of input image;

If it is determined that the type of the input picture is a color picture, acquiring a compression quality factor indicating a compression rate of the color picture;

performing scaling processing on the color picture; and

According to the compression quality factor of the color picture, the image sharpening process is performed on the color picture obtained after scaling processing to obtain an output color picture.

A2. The method according to A1, wherein the higher the compression rate indicated by the compression quality factor, the lower the sharpness of image sharpening.

A3. According to the method described in A1, the step of performing image sharpening processing on the color picture obtained after the scaling process includes: performing convolution processing on the color picture obtained after the scaling process and Gaussian signals indicating different sharpening degrees, The output color picture is obtained, wherein the sharpness indicated by the Gaussian signal is determined by a parameter in the Gaussian signal, and the size of the parameter in the Gaussian signal is determined according to the compression quality factor of the color picture.

A4. According to the method described in any one of A1 to A3, before the zooming processing of the color picture, it also includes: converting the format of the color picture from the original format to a streaming network graphics format;

After the zooming process on the color picture, it further includes: converting the format of the color picture obtained after the scaling process from the streaming network graphics format to the original format.

A5. According to the method described in any one of A1 to A4, the step of obtaining the compression quality factor of the color picture includes: obtaining the compression quality factor of the color picture by analyzing the content of the color picture, and calculating the compression quality factor Factors converted to metric levels by the independent JPEG group.

A6. The method according to any one of A1 to A5, further comprising:

When it is determined that the type of the input picture is a grayscale picture, performing scaling processing on the grayscale picture;

Perform histogram equalization processing on the grayscale image obtained after the scaling process to obtain an output grayscale image.

The present invention also discloses: B7, a processing device based on image type, comprising:

A judging module, suitable for judging the type of the input picture;

A compression quality factor determination module, adapted to acquire a compression quality factor indicating the compression rate of the color picture when the judging module determines that the type of the input picture is a color picture;

A first scaling module, adapted to perform scaling processing on the color picture; and

The sharpening module is adapted to perform image sharpening processing on the color picture obtained after zooming according to the compression quality factor of the color picture to obtain an output color picture.

B8. The device according to B7, the higher the compression rate indicated by the compression quality factor, the lower the sharpening degree of the image sharpening performed by the sharpening module.

B9. According to the device described in B7, the sharpening module includes:

The sharpening processing module is adapted to perform convolution processing on the color picture obtained after zooming processing and Gaussian signals indicating different sharpening degrees to obtain the output color picture;

The parameter acquisition module is adapted to determine the size of the parameter in the Gaussian signal according to the compression quality factor of the color picture, and the sharpness indicated by the Gaussian signal is determined by the parameter in the Gaussian signal.

B10. The equipment described in B7 or B8 or B9, further comprising:

The format conversion module is suitable for converting the format of the color picture from the original format to the streaming network graphics format, and converting the format of the color picture obtained after scaling from the streaming network graphics format to the original format.

B11. According to the device according to any one of B7 to B10, the compression quality factor determination module is specifically adapted to obtain the compression quality factor of the color picture by analyzing the content of the color picture, and convert the compression quality factor into an independent JPEG The metric level of the group.

B12. The device according to any one of B7 to B11, further comprising:

The second scaling module is adapted to perform scaling processing on the grayscale picture when the judging module determines that the type of the input picture is a grayscale picture;

The histogram equalization processing module is suitable for performing histogram equalization processing on the grayscale image obtained after scaling processing to obtain an output grayscale image.

Claims (10)

1. A processing method based on image type, comprising: Determine the type of input image; If it is determined that the type of the input picture is a color picture, acquiring a compression quality factor indicating a compression rate of the color picture; performing scaling processing on the color picture; and According to the compression quality factor of the color picture, the image sharpening process is performed on the color picture obtained after scaling processing to obtain an output color picture. 2. The method of claim 1, wherein the higher the compression ratio indicated by the compression quality factor, the lower the sharpness of image sharpening. 3. The method according to claim 1, the step of performing image sharpening processing on the color picture obtained after the scaling process comprises: convolving the color picture obtained after the scaling process with Gaussian signals indicating different degrees of sharpness processing to obtain the output color picture, wherein the sharpness indicated by the Gaussian signal is determined by a parameter in the Gaussian signal, and the size of the parameter in the Gaussian signal is determined according to the compression quality factor of the color picture. 4. The method according to any one of claims 1 to 3, further comprising: converting the format of the color picture from an original format to a streaming network graphics format before the scaling process of the color picture; After the zooming process on the color picture, it further includes: converting the format of the color picture obtained after the scaling process from the streaming network graphics format to the original format. 5. The method according to any one of claims 1 to 4, the step of obtaining the compression quality factor of the color picture comprises: obtaining the compression quality factor of the color picture by analyzing the content of the color picture, and converting the The compression quality factor translates into a metric rating from the independent JPEG group. 6. The method according to any one of claims 1 to 5, further comprising: When it is determined that the type of the input picture is a grayscale picture, performing scaling processing on the grayscale picture; Perform histogram equalization processing on the grayscale image obtained after the scaling process to obtain an output grayscale image. 7. A processing device based on image type, comprising: A judging module, suitable for judging the type of the input picture; A compression quality factor determination module, adapted to acquire a compression quality factor indicating the compression rate of the color picture when the judging module determines that the type of the input picture is a color picture; A first scaling module, adapted to perform scaling processing on the color picture; and The sharpening module is adapted to perform image sharpening processing on the color picture obtained after zooming according to the compression quality factor of the color picture to obtain an output color picture. 8. The device according to claim 7, the higher the compression rate indicated by the compression quality factor, the lower the sharpening degree of image sharpening performed by the sharpening module. 9. The device of claim 7, the sharpening module comprising: The sharpening processing module is adapted to perform convolution processing on the color picture obtained after zooming processing and Gaussian signals indicating different sharpening degrees to obtain the output color picture; The parameter acquisition module is adapted to determine the size of the parameter in the Gaussian signal according to the compression quality factor of the color picture, and the sharpness indicated by the Gaussian signal is determined by the parameter in the Gaussian signal. 10. Apparatus according to claim 7 or 8 or 9, further comprising: The format conversion module is suitable for converting the format of the color picture from the original format to the streaming network graphics format, and converting the format of the color picture obtained after scaling from the streaming network graphics format to the original format.