CN109510986B - Self-adaptive texture gradual change prediction method in bandwidth compression - Google Patents
Self-adaptive texture gradual change prediction method in bandwidth compression Download PDFInfo
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Abstract
The invention relates to a self-adaptive texture gradual change prediction method in bandwidth compression, which comprises the following steps: determining a sampling mode of a current MB; determining a sampling point of the current MB by using the sampling mode; selecting a prediction mode to predict the current MB, and acquiring a prediction residual error of the current MB; calculating a sum of absolute values of residuals for the current MB; and determining the prediction mode of the current MB according to the residual absolute value sum. The invention calculates the prediction residual error and SAD of the current prediction macro block by defining the sampling mode of the MB. Compared with the existing method, when the texture of the image to be compressed is complex, the prediction residual error is obtained for the MB at the texture boundary of the current image according to the gradual change principle of the texture without depending on the MB around the current MB through the texture characteristic of the current MB, so that the precision of solving the prediction residual error value for the complex texture area can be improved, the theoretical limit entropy is further reduced, and the bandwidth compression ratio is increased.
Description
Technical Field
The invention relates to the technical field of compression, in particular to a self-adaptive texture gradual change prediction method in bandwidth compression.
Background
With the rapid development and widespread use of multimedia technology and network technology, video data transmitted through a network is becoming more and more. Because the original video data requires a very large bandwidth and has a very large redundancy, the video data is usually encoded and compressed before being transmitted, in which case, it is necessary to improve the storage space and transmission bandwidth of the image by using the on-chip bandwidth compression technique.
The goal of the bandwidth compression technology is to increase the compression multiple as much as possible and reduce the occupation of Double Data Rate (DDR) with less logic area cost. Bandwidth compression is mainly composed of four parts, including: the device comprises a prediction module, a quantization module, a code control module and an entropy coding module. The prediction module is an important module, and utilizes the spatial redundancy existing between adjacent pixels of the image. Although the image data is irregular, some pixels are the same or a small block of graphics are the same from the aspect of bits and pixels of the expression information, prediction is carried out according to the correlation among the pixels, and the standard deviation of the prediction difference is far smaller than that of the original image data, so that the prediction difference is encoded, the theoretical entropy of the image data is more favorably minimized, and the purpose of improving the compression efficiency is achieved. The algorithms of the current prediction module are mainly divided into two types, namely texture related prediction and pixel value related prediction.
In the conventional texture-related prediction method, for a macroblock (Macro block, abbreviated as MB) at a texture boundary in an image, because a current MB and a surrounding MB are not in the same texture region, a correlation between the current MB and the surrounding MB is poor, that is, a smaller prediction residual error cannot be obtained through the correlation between the current MB and the surrounding MB.
Disclosure of Invention
Therefore, in order to solve the technical defects and shortcomings in the prior art, the invention provides a self-adaptive texture gradual change prediction method in bandwidth compression.
Specifically, an embodiment of the present invention provides a method for predicting adaptive texture gradient in bandwidth compression, including:
determining a sampling mode of a current MB;
determining a sampling point of the current MB by using the sampling mode;
selecting a prediction mode to predict the current MB, and acquiring a prediction residual error of the current MB;
calculating a sum of absolute values of residuals for the current MB;
and determining the prediction mode of the current MB according to the residual absolute value sum.
In one embodiment of the present invention, determining the sample point of the current MB by using the sampling manner includes:
and determining the sampling point of the current MB by using a pixel value inflection point sampling mode.
In an embodiment of the present invention, determining the sampling point of the current MB by using a pixel value inflection point sampling method includes:
obtaining a pixel difference value of the current MB by subtracting a pixel value of a current pixel component of the current MB from a pixel value of a pixel component adjacent to the current pixel component;
setting the last bit of the continuous value in the pixel difference value as a pixel value inflection point;
and setting the pixel component of the current MB corresponding to the pixel value inflection point as the sampling point of the current MB.
In one embodiment of the present invention, setting the last bit of consecutive values in the pixel difference value as a pixel value inflection point comprises:
setting the last positive value of the continuous positive values in the pixel difference value as a first pixel value inflection point;
setting a last negative value of consecutive negative values in the pixel difference value as a second pixel value inflection point.
In an embodiment of the present invention, selecting a prediction mode to predict the current MB and obtaining a prediction residual of the current MB includes:
selecting a prediction mode to predict the current MB, and calculating a prediction residual error of the current MB sampling point;
and selecting a prediction mode to predict the current MB, and calculating the prediction residual error of the non-sampling point of the current MB.
In an embodiment of the present invention, the prediction mode includes N angle prediction modes.
In one embodiment of the present invention, the angle prediction modes include 45 degree texture prediction, 90 degree texture prediction and 135 degree texture prediction.
In one embodiment of the present invention, the 45-degree texture prediction includes:
selecting an adjacent MB right above the current MB;
selecting pixel components in the direction of 45 degrees of the current MB sampling point in the immediately-above adjacent MB;
and predicting the pixel values of the sampling points and the pixel values of the pixel components in the 45-degree direction.
In one embodiment of the present invention, the 90-degree texture prediction comprises:
selecting an adjacent MB right above the current MB;
selecting pixel components in the direction of 90 degrees of the current MB sampling point in the immediately-above adjacent MB;
and predicting the pixel values of the sampling points and the pixel values of the pixel components in the 90-degree direction.
In one embodiment of the present invention, the 135 degree texture prediction comprises:
selecting an adjacent MB right above the current MB;
selecting a pixel component in the direction of 135 degrees of the current MB sampling point in an immediately upper adjacent MB;
and predicting the pixel values of the sampling points and the pixel values of the pixel components in the 135-degree direction.
Based on this, the invention has the following advantages:
the invention calculates the prediction residual error and SAD of the current macro block by defining the sampling mode of MB and the reference mode of pixel component prediction. Compared with the prior art, when the texture of the image to be compressed is complex, the prediction residual error is obtained for the MB at the texture boundary of the current image according to the gradual change principle of the texture without depending on the peripheral MB of the current MB through the texture characteristic of the current MB, so that the precision of solving the prediction residual error value for the complex texture area can be improved, the theoretical limit entropy is further reduced, and the bandwidth compression ratio is increased.
Other aspects and features of the present invention will become apparent from the following detailed description, which proceeds with reference to the accompanying drawings. It is to be understood, however, that the drawings are designed solely for purposes of illustration and not as a definition of the limits of the invention, for which reference should be made to the appended claims. It should be further understood that the drawings are not necessarily drawn to scale and that, unless otherwise indicated, they are merely intended to conceptually illustrate the structures and procedures described herein.
Drawings
The following detailed description of embodiments of the invention will be made with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart of a method for predicting adaptive texture gradient in bandwidth compression according to an embodiment of the present invention;
fig. 2 is a schematic diagram of another adaptive texture gradient prediction method according to an embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below.
Example one
Referring to fig. 1, fig. 1 is a schematic flow chart of a method for predicting adaptive texture gradient in bandwidth compression according to an embodiment of the present invention; this embodiment describes a prediction method provided by the present invention in detail, and the prediction method includes the following steps:
step 1, determining a sampling mode of a current MB;
step 3, selecting a prediction mode to predict the current MB, and acquiring a prediction residual error of the current MB;
and 5, determining the prediction mode of the current MB according to the residual absolute value sum.
Wherein, step 2 may include the following steps:
and step 21, determining the sampling point of the current MB by using a pixel value inflection point sampling mode.
Wherein, the step 21 may include the steps of:
step 211, obtaining a pixel difference value of the current MB by subtracting a pixel value of a current pixel component of the current MB from a pixel value of a pixel component adjacent to the current pixel component;
step 212, setting the last bit of the continuous value in the pixel difference value as a pixel value inflection point;
step 213, setting the pixel component of the current MB corresponding to the pixel value inflection point as the sampling point of the current MB.
Wherein step 212 may comprise the steps of:
step 2121, setting the last positive value of the continuous positive values in the pixel difference value as a first pixel value inflection point;
and step 2122, setting the last negative value of the continuous negative values in the pixel difference value as a second pixel value inflection point.
Wherein, step 3 may include:
step 31, selecting a prediction mode to predict the current MB, and calculating a prediction residual error of the current MB sampling point;
and step 32, selecting a prediction mode to predict the current MB, and calculating the prediction residual error of the non-sampling point of the current MB.
Preferably, the prediction mode includes N angle prediction modes.
Preferably, the angle prediction modes include 45-degree texture prediction, 90-degree texture prediction and 135-degree texture prediction.
Preferably, the 45-degree texture prediction comprises:
selecting an adjacent MB right above the current MB;
selecting pixel components in the direction of 45 degrees of the current MB sampling point in the immediately-above adjacent MB;
and predicting the pixel values of the sampling points and the pixel values of the pixel components in the 45-degree direction.
Preferably, the 90-degree texture prediction comprises:
selecting an adjacent MB right above the current MB;
selecting pixel components in the direction of 90 degrees of the current MB sampling point in the immediately-above adjacent MB;
and predicting the pixel values of the sampling points and the pixel values of the pixel components in the 90-degree direction.
Preferably, the 135-degree texture prediction comprises:
selecting an adjacent MB right above the current MB;
selecting a pixel component in the direction of 135 degrees of the current MB sampling point in an immediately upper adjacent MB;
and predicting the pixel values of the sampling points and the pixel values of the pixel components in the 135-degree direction.
Example two
Referring to fig. 2, fig. 2 is a schematic diagram illustrating an adaptive texture gradient prediction method according to an embodiment of the present invention. The present embodiment describes a prediction method proposed by the present invention in detail on the basis of the above embodiments. The prediction method comprises the following steps:
step 1, defining the size of MB;
defining the size of MB as m × n pixel components, wherein m is more than or equal to 1, and n is more than or equal to 1;
preferably, the size of MB may be defined as 8 × 1 pixel components, 16 × 1 pixel components, 32 × 1 pixel components, 64 × 1 pixel components; in the present embodiment, the size of the MB is 16 × 1 pixel components, and the other MBs with different sizes are the same.
As shown in fig. 2, the pixel values of 16 × 1 pixel components in the MB are set to 12, 14, 15, 18, 20, 23, 15, 10, 4, 0, 2, 4, 5, and 6 in this order from left to right.
step 201, according to the texture correlation existing in the MB, detecting the texture gradual change of the MB, determining a texture gradual change point of the MB, and setting the texture gradual change point of the MB as a pixel value inflection point.
Specifically, the pixel value of the current pixel component in the current MB is subtracted from the pixel value of the adjacent pixel component in the current MB, and as shown in fig. 2, the pixel value of the current pixel component in the current MB is subtracted from the pixel value of the previous pixel component in the current MB, so as to solve the pixel residual value of the current MB. The pixel residual values at the corresponding positions in the current MB are 12, 2, 1, 3, 2, 3, -8, -5, -6, -4, 2, 0, 2, 1, 0, 1 from left to right in sequence.
Step 202, setting the last value of consecutive positive values or consecutive negative values in the pixel residual values as a pixel value inflection point, wherein the value with the pixel residual value of 0 is not set as the pixel value inflection point.
Step 203, setting the position corresponding to the current pixel component corresponding to the pixel value inflection point as a sampling point, and setting the points at the first and last positions in the current pixel component as sampling points.
Preferably, as shown in fig. 2, the pixel value inflection points in the obtained pixel residual values are 3 and-4, and the current pixel components 23 and 0 and the first and last pixel components corresponding to the pixel value inflection points 3 and-4 are set as sampling points. The pixel components 12, 23, 0, and 6 corresponding to the original point form 4 sampling points.
And 3, predicting the sampling point in the current MB and the MB right above the sampling point. The prediction modes are 135-degree prediction, 45-degree prediction and 90-degree prediction. Namely, the sampling point in the current MB and the 45-degree pixel component point, the 90-degree pixel component point and the 135-degree pixel component point corresponding to the sampling point in the adjacent MB right above the current MB are respectively predicted, and the prediction residual and the sum of absolute values (SAD for short) of the residual are solved. Specifically, a 45-degree pixel component point, a 90-degree pixel component point, and a 135-degree pixel component point corresponding to a sampling point in an adjacent MB directly above the current MB may be subtracted from the sampling point in the current MB, so as to obtain a prediction residual error; and respectively taking absolute values of the prediction residuals of each sampling point under each prediction mode, and adding the absolute values to obtain the sum of absolute values of the residuals. And finally, selecting a prediction mode with the minimum SAD as a sampling point prediction mode of the current MB, and acquiring the prediction residual of the prediction mode.
And 4, solving the prediction residual error of the non-sampling point by using a formula for the non-sampling point in the current MB, wherein the formula is as follows:
Resi＝(sample1-sample0)*(i+1)/(num+1)
sample0 and sample1 in the formula are pixel component reconstruction values of consecutive sample points of the current MB, i is an index of a non-sample point, and num is the number of non-sample points.
Further, the pixel component reconstruction value may refer to a pixel component value reconstructed by the decoding end of the compressed coded MB.
And 5, writing the first-position-removed sampling point, the prediction mode and the prediction residual in the current MB into a code stream.
In summary, the present invention is described based on the adaptive texture gradient prediction method in bandwidth compression by applying specific examples, and the description of the above embodiments is only used to help understanding the method of the present invention and its core idea; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention, and the scope of the present invention should be subject to the appended claims.
Claims (2)
1. A method for predicting adaptive texture gradual change in bandwidth compression is characterized by comprising the following steps:
determining a sampling mode of a current MB;
determining a sample point of the current MB using the sampling pattern, comprising: determining the sampling point of the current MB by using a pixel value inflection point sampling mode; wherein the determining the sample point of the current MB in the pixel value inflection point sampling manner includes: obtaining a pixel difference value of the current MB by subtracting a pixel value of a current pixel component of the current MB from a pixel value of a pixel component adjacent to the current pixel component; setting the last bit of the continuous value in the pixel difference value as a pixel value inflection point; setting the pixel component of the current MB corresponding to the pixel value inflection point as a sampling point of the current MB;
selecting a prediction mode to predict the current MB, and acquiring a prediction residual error of the current MB;
calculating a sum of absolute values of residuals for the current MB;
determining a prediction mode of the current MB according to the residual absolute value;
selecting a prediction mode to predict the current MB, acquiring a prediction residual of the current MB, and calculating the absolute value sum of the residual of the current MB; determining a prediction mode of the current MB according to the residual absolute value; the method comprises the following steps:
selecting a prediction mode to predict the current MB, and calculating a prediction residual error of the current MB sampling point; the prediction modes are three angle prediction modes of 135-degree prediction, 45-degree prediction and 90-degree prediction, and the calculation method of the prediction residual error of the current MB sampling point comprises the following steps: respectively subtracting the 45-degree pixel component point, the 90-degree pixel component point and the 135-degree pixel component point corresponding to the sampling point in the adjacent MB right above the current MB from the sampling point in the current MB to obtain a prediction residual error; respectively taking absolute values of the prediction residual errors of each sampling point under each prediction mode and then adding the absolute values to obtain the sum of absolute values of the residual errors; finally, selecting a prediction mode with the minimum residual absolute value and the minimum residual absolute value as a sampling point prediction mode of the current MB, and acquiring a prediction residual of the prediction mode;
selecting a prediction mode to predict the current MB, and solving a prediction residual error of a non-sampling point by using a formula, wherein the formula is as follows:
Resi＝(sample1-sample0)*(i+1)/(num+1)
sample0 and sample1 in the formula are pixel component reconstruction values of consecutive sample points of the current MB, i is an index of a non-sample point, and num is the number of non-sample points.
2. The method of claim 1, wherein setting a last bit of consecutive values in the pixel difference value as a pixel value inflection point comprises:
setting the last positive value of the continuous positive values in the pixel difference value as a first pixel value inflection point;
setting a last negative value of consecutive negative values in the pixel difference value as a second pixel value inflection point.
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