Ouyang et al., 2023 - Google Patents
A Dynamic Codec with Adaptive Quantization for Convolution Neural NetworkOuyang et al., 2023
- Document ID
- 5994178867600325451
- Author
- Ouyang Y
- Wang X
- Shi G
- Chen L
- An F
- Publication year
- Publication venue
- 2023 IEEE 15th International Conference on ASIC (ASICON)
External Links
Snippet
Current convolutional neural networks (CNN) have achieved a high inference accuracy by deeper architecture, generating a large amount of interlayer data. Limited to on-chip memory, massive feature maps significantly impact the performance of hardware platforms …
- 238000013139 quantization 0 title abstract description 34
Classifications
-
- H—ELECTRICITY
- H03—BASIC ELECTRONIC CIRCUITRY
- H03M—CODING; DECODING; CODE CONVERSION IN GENERAL
- H03M7/00—Conversion of a code where information is represented by a given sequence or number of digits to a code where the same information or similar information or a subset of information is represented by a different sequence or number of digits
- H03M7/30—Compression; Expansion; Suppression of unnecessary data, e.g. redundancy reduction
- H03M7/3082—Vector coding
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T9/00—Image coding, e.g. from bit-mapped to non bit-mapped
- G06T9/007—Transform coding, e.g. discrete cosine transform
-
- H—ELECTRICITY
- H03—BASIC ELECTRONIC CIRCUITRY
- H03M—CODING; DECODING; CODE CONVERSION IN GENERAL
- H03M7/00—Conversion of a code where information is represented by a given sequence or number of digits to a code where the same information or similar information or a subset of information is represented by a different sequence or number of digits
- H03M7/30—Compression; Expansion; Suppression of unnecessary data, e.g. redundancy reduction
- H03M7/40—Conversion to or from variable length codes, e.g. Shannon-Fano code, Huffman code, Morse code
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T9/00—Image coding, e.g. from bit-mapped to non bit-mapped
- G06T9/008—Vector quantisation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T9/00—Image coding, e.g. from bit-mapped to non bit-mapped
- G06T9/005—Statistical coding, e.g. Huffman, run length coding
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T9/00—Image coding, e.g. from bit-mapped to non bit-mapped
- G06T9/001—Model-based coding, e.g. wire frame
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Sonal | A study of various image compression techniques | |
CN115361559B (en) | Image encoding method, image decoding method, device and storage medium | |
Boopathi et al. | An image compression approach using wavelet transform and modified self organizing map | |
Kumar et al. | A review: DWT-DCT technique and arithmetic-Huffman coding based image compression | |
Zhou et al. | Image compression based on discrete cosine transform and multistage vector quantization | |
Sun et al. | Dictionary learning for image coding based on multisample sparse representation | |
Li et al. | Image compression using transformed vector quantization | |
CN107170020B (en) | A Dictionary Learning Lossy Compression Method for Still Images Based on Minimum Quantization Error Criterion | |
Zhe et al. | Rate-distortion optimized coding for efficient cnn compression | |
Garg et al. | Analysis of different image compression techniques: a review | |
Zhang et al. | SAR image compression using discretized Gaussian adaptive model and generalized subtractive normalization | |
Maghari | A comparative study of DCT and DWT image compression techniques combined with Huffman coding | |
Karthikeyan et al. | An efficient image compression method by using optimized discrete wavelet transform and Huffman encoder | |
Ouyang et al. | A Dynamic Codec with Adaptive Quantization for Convolution Neural Network | |
Ranganathan et al. | Learned Image compression with discretized gaussian mixture likelihoods and attention modules | |
Chang et al. | A Reversible Data Hiding Method for SMVQ Indices Based on Improved Locally Adaptive Coding. | |
Ahmed et al. | Images Compression using Combined Scheme of Transform Coding | |
Amin et al. | Vector quantization based lossy image compression using wavelets–a review | |
Garg et al. | Various Image Compression Techniques: A Review. | |
Srimanth et al. | Implementation Challenges and Performance Analysis of Image Compression Using Huffman Encoding and DCT Algorithm on DSP Processor TMS320C6748 and Arduino Nano 33 BLE | |
Liguori | Pyramid vector quantization for deep learning | |
Kunwar | Strategies in JPEG compression using convolutional neural network (CNN) | |
S Mahdi et al. | Adaptive Color Image Compression Using ADJPEG and ISUQ of Hierarchical Decomposition Scheme | |
Guzmán et al. | Morphological transform for image compression | |
Krishna et al. | Incorporation of DCT and MSVQ to Enhance Image Compression Ratio of an image |