The Experts below are selected from a list of 2154 Experts worldwide ranked by ideXlab platform
Enhui Yang - One of the best experts on this subject based on the ideXlab platform.
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Quantization Table design revisited for image/video coding.
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 2014Co-Authors: Enhui Yang, Chang Sun, Jin MengAbstract:Quantization Table design is revisited for image/video coding where soft decision Quantization (SDQ) is considered. Unlike conventional approaches, where Quantization Table design is bundled with a specific encoding method, we assume optimal SDQ encoding and design a Quantization Table for the purpose of reconstruction. Under this assumption, we model transform coefficients across different frequencies as independently distributed random sources and apply the Shannon lower bound to approximate the rate distortion function of each source. We then show that a Quantization Table can be optimized in a way that the resulting distortion complies with certain behavior. Guided by this new design principle, we propose an efficient statistical-model-based algorithm using the Laplacian model to design Quantization Tables for DCT-based image coding. When applied to standard JPEG encoding, it provides more than 1.5-dB performance gain in PSNR, with almost no extra burden on complexity. Compared with the state-of-the-art JPEG Quantization Table optimizer, the proposed algorithm offers an average 0.5-dB gain in PSNR with computational complexity reduced by a factor of more than 2000 when SDQ is OFF, and a 0.2-dB performance gain or more with 85% of the complexity reduced when SDQ is ON. Significant compression performance improvement is also seen when the algorithm is applied to other image coding systems proposed in the literature.
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Quantization Table design revisited for image video coding
IEEE Transactions on Image Processing, 2014Co-Authors: Enhui Yang, Chang Sun, Jin MengAbstract:Quantization Table design is revisited for image/video coding where soft decision Quantization (SDQ) is considered. Unlike conventional approaches, where Quantization Table design is bundled with a specific encoding method, we assume optimal SDQ encoding and design a Quantization Table for the purpose of reconstruction. Under this assumption, we model transform coefficients across different frequencies as independently distributed random sources and apply the Shannon lower bound to approximate the rate distortion function of each source. We then show that a Quantization Table can be optimized in a way that the resulting distortion complies with certain behavior. Guided by this new design principle, we propose an efficient statistical-model-based algorithm using the Laplacian model to design Quantization Tables for DCT-based image coding. When applied to standard JPEG encoding, it provides more than 1.5-dB performance gain in PSNR, with almost no extra burden on complexity. Compared with the state-of-the-art JPEG Quantization Table optimizer, the proposed algorithm offers an average 0.5-dB gain in PSNR with computational complexity reduced by a factor of more than 2000 when SDQ is OFF, and a 0.2-dB performance gain or more with 85% of the complexity reduced when SDQ is ON. Significant compression performance improvement is also seen when the algorithm is applied to other image coding systems proposed in the literature.
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Quantization Table design revisited for image video coding
International Conference on Image Processing, 2013Co-Authors: Enhui Yang, Chang Sun, Jin MengAbstract:Quantization Table design is revisited for image/video coding where soft decision Quantization (SDQ) is considered. Unlike conventional approaches where Quantization Table design is bundled with a specific encoding method, we assume optimal SDQ encoding and design a Quantization Table for the purpose of reconstruction. Under this assumption, we model transform coefficients across different frequencies as independently distributed random sources and apply the Shannon lower bound to approximate the rate distortion function of each source. We then show that a Quantization Table can be optimized in a way that the resulting distortion complies with certain behavior. Lastly, guided by this new theoretical result, we propose an efficient statistical-model-based algorithm using the Laplacian model to design Quantization Tables for JPEG encoding. Compared with the state of the art, the proposed algorithm provides an average 0.5 dB gain in PSNR with computational complexity reduced by a factor of more than 2000 when SDQ is off, and a 0.1 dB performance gain with 85% of the complexity reduced when SDQ is on.
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ICIP - Quantization Table design revisited for image/video coding
2013 IEEE International Conference on Image Processing, 2013Co-Authors: Enhui Yang, Chang Sun, Jin MengAbstract:Quantization Table design is revisited for image/video coding where soft decision Quantization (SDQ) is considered. Unlike conventional approaches where Quantization Table design is bundled with a specific encoding method, we assume optimal SDQ encoding and design a Quantization Table for the purpose of reconstruction. Under this assumption, we model transform coefficients across different frequencies as independently distributed random sources and apply the Shannon lower bound to approximate the rate distortion function of each source. We then show that a Quantization Table can be optimized in a way that the resulting distortion complies with certain behavior. Lastly, guided by this new theoretical result, we propose an efficient statistical-model-based algorithm using the Laplacian model to design Quantization Tables for JPEG encoding. Compared with the state of the art, the proposed algorithm provides an average 0.5 dB gain in PSNR with computational complexity reduced by a factor of more than 2000 when SDQ is off, and a 0.1 dB performance gain with 85% of the complexity reduced when SDQ is on.
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dithered soft decision Quantization for baseline jpeg encoding and its joint optimization with huffman coding and Quantization Table selection
Asilomar Conference on Signals Systems and Computers, 2011Co-Authors: Enhui Yang, Chang SunAbstract:Based on baseline JPEG, a new image coding framework is first developed, where dithered (uniform) quantizers are used to replace JPEG uniform quantizers for the purpose of improving the rate-distortion performance without sacrificing the coding complexity instead of the conventional subjective image quality. By combining dithering with soft decision Quantization (SDQ)—yielding dithered SDQ, an iterative algorithm is then proposed for jointly designing dithers for DCT coefficients at each frequency (i.e., dither Table), Quantization Table, run-length coding, and Huffman coding. The algorithm converges in the sense that its rate-distortion cost is monotonically decreasing until a stationary point is reached. When compared with state-of-the-art baseline JPEG R-D optimizer proposed recently by Yang and Wang, our algorithm achieves comparable and sometimes better rate-distortion performance with 65% computational complexity reduction.
Wen Gao - One of the best experts on this subject based on the ideXlab platform.
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Just-Noticeable Difference-Based Perceptual Optimization for JPEG Compression
IEEE Signal Processing Letters, 2017Co-Authors: Xinfeng Zhang, Shiqi Wang, Weisi Lin, Wen GaoAbstract:The Quantization Table in JPEG, which specifies the Quantization scale for each discrete cosine transform (DCT) coefficient, plays an important role in image codec optimization. However, the generic Quantization Table design that is based on the characteristics of human visual system (HVS) cannot adapt to the variations of image content. In this letter, we propose a just-noticeable difference (JND) based Quantization Table derivation method for JPEG by optimizing the rate-distortion costs for all the frequency bands. To achieve better perceptual quality, the DCT domain JND-based distortion metric is utilized to model the stair distortion perceived by HVS. The rate-distortion cost for each band is derived by estimating the rate with the first-order entropy of quantized coefficients. Subsequently, the optimal Quantization Table is obtained by minimizing the total rate-distortion costs of all the bands. Extensive experimental results show that the Quantization Table generated by the proposed method achieves significant bit-rate savings compared with JPEG recommended Quantization Table and specifically developed Quantization Tables in terms of both objective and subjective evaluations.
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joint optimization of jpeg Quantization Table and coefficient thresholding for low bitrate mobile visual search
International Conference on Image Processing, 2014Co-Authors: Yitong Wang, Lingyu Duan, Tiejun Huang, Jie Lin, Wen GaoAbstract:Low latency query delivery over wireless network is a key problem for mobile visual search. Extracting compact descriptors directly on the mobile device is computational expensive, an alternate approach is to send highly compressed JPEG query images. As JPEG baseline optimizes the rate-distortion from a perceptual perspective rather than maintaining search performance, recent work proposed to learn a feature-preserving JPEG Quantization Table for improved search accuracy. However, this method is data-dependent and the Quantization Table cannot adapt to image blocks. To address these issues, we propose to jointly optimize the JPEG Quantization Table and coefficient thresholding. The matching score between uncompressed image and its compressed JPEG image is employed as the distortion measure to avoid time consuming image labeling, and coefficient thresholding eliminates the redundant coefficients. Extensive experiments on benchmark datasets show that our approach obtains superior performance than state-of-the-art at low bitrates, meanwhile, it consumes lower cost including processing time, memory and battery on mobile device.
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ICIP - Joint optimization of JPEG Quantization Table and coefficient thresholding for low bitrate mobile visual search
2014 IEEE International Conference on Image Processing (ICIP), 2014Co-Authors: Yitong Wang, Lingyu Duan, Tiejun Huang, Jie Lin, Wen GaoAbstract:Low latency query delivery over wireless network is a key problem for mobile visual search. Extracting compact descriptors directly on the mobile device is computational expensive, an alternate approach is to send highly compressed JPEG query images. As JPEG baseline optimizes the rate-distortion from a perceptual perspective rather than maintaining search performance, recent work proposed to learn a feature-preserving JPEG Quantization Table for improved search accuracy. However, this method is data-dependent and the Quantization Table cannot adapt to image blocks. To address these issues, we propose to jointly optimize the JPEG Quantization Table and coefficient thresholding. The matching score between uncompressed image and its compressed JPEG image is employed as the distortion measure to avoid time consuming image labeling, and coefficient thresholding eliminates the redundant coefficients. Extensive experiments on benchmark datasets show that our approach obtains superior performance than state-of-the-art at low bitrates, meanwhile, it consumes lower cost including processing time, memory and battery on mobile device.
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optimizing jpeg Quantization Table for low bit rate mobile visual search
Visual Communications and Image Processing, 2012Co-Authors: Lingyu Duan, Xiangkai Liu, Jie Chen, Tiejun Huang, Wen GaoAbstract:Smart phones is bringing about emerging potentials in mobile visual search. Extensive research efforts have been made in compact visual descriptors. However, directly extracting visual descriptors on a mobile device is computationally intensive and time consuming. Towards low bit rate visual search, we propose to deeply compress query images by learning a customized JPEG Quantization Table in the context of visual search. Distinct from traditional image compression, by incorporating pair-wise image matching precision into distortion measure, we optimize Quantization Table to seek a better trade-off between image compression rate and visual search performance. An evolutionary algorithm is employed to learn an optimal Quantization Table. Under MPEG CDVS evaluation framework, extensive evaluation has been done including image retrieval and pair-wise matching over 1 million database images. Experimental results have demonstrated that our optimized Quantization Table works much better than JPEG default one in terms of retrieval/matching performance vs. a set of different operating points. The proposed low bit rate solution may be easily deployed to smart phones without hardware support, as a useful complement to the ongoing MPEG CDVS standardization efforts.
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VCIP - Optimizing JPEG Quantization Table for low bit rate mobile visual search
2012 Visual Communications and Image Processing, 2012Co-Authors: Lingyu Duan, Xiangkai Liu, Jie Chen, Tiejun Huang, Wen GaoAbstract:Smart phones is bringing about emerging potentials in mobile visual search. Extensive research efforts have been made in compact visual descriptors. However, directly extracting visual descriptors on a mobile device is computationally intensive and time consuming. Towards low bit rate visual search, we propose to deeply compress query images by learning a customized JPEG Quantization Table in the context of visual search. Distinct from traditional image compression, by incorporating pair-wise image matching precision into distortion measure, we optimize Quantization Table to seek a better trade-off between image compression rate and visual search performance. An evolutionary algorithm is employed to learn an optimal Quantization Table. Under MPEG CDVS evaluation framework, extensive evaluation has been done including image retrieval and pair-wise matching over 1 million database images. Experimental results have demonstrated that our optimized Quantization Table works much better than JPEG default one in terms of retrieval/matching performance vs. a set of different operating points. The proposed low bit rate solution may be easily deployed to smart phones without hardware support, as a useful complement to the ongoing MPEG CDVS standardization efforts.
Jin Meng - One of the best experts on this subject based on the ideXlab platform.
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Quantization Table design revisited for image/video coding.
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 2014Co-Authors: Enhui Yang, Chang Sun, Jin MengAbstract:Quantization Table design is revisited for image/video coding where soft decision Quantization (SDQ) is considered. Unlike conventional approaches, where Quantization Table design is bundled with a specific encoding method, we assume optimal SDQ encoding and design a Quantization Table for the purpose of reconstruction. Under this assumption, we model transform coefficients across different frequencies as independently distributed random sources and apply the Shannon lower bound to approximate the rate distortion function of each source. We then show that a Quantization Table can be optimized in a way that the resulting distortion complies with certain behavior. Guided by this new design principle, we propose an efficient statistical-model-based algorithm using the Laplacian model to design Quantization Tables for DCT-based image coding. When applied to standard JPEG encoding, it provides more than 1.5-dB performance gain in PSNR, with almost no extra burden on complexity. Compared with the state-of-the-art JPEG Quantization Table optimizer, the proposed algorithm offers an average 0.5-dB gain in PSNR with computational complexity reduced by a factor of more than 2000 when SDQ is OFF, and a 0.2-dB performance gain or more with 85% of the complexity reduced when SDQ is ON. Significant compression performance improvement is also seen when the algorithm is applied to other image coding systems proposed in the literature.
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Quantization Table design revisited for image video coding
IEEE Transactions on Image Processing, 2014Co-Authors: Enhui Yang, Chang Sun, Jin MengAbstract:Quantization Table design is revisited for image/video coding where soft decision Quantization (SDQ) is considered. Unlike conventional approaches, where Quantization Table design is bundled with a specific encoding method, we assume optimal SDQ encoding and design a Quantization Table for the purpose of reconstruction. Under this assumption, we model transform coefficients across different frequencies as independently distributed random sources and apply the Shannon lower bound to approximate the rate distortion function of each source. We then show that a Quantization Table can be optimized in a way that the resulting distortion complies with certain behavior. Guided by this new design principle, we propose an efficient statistical-model-based algorithm using the Laplacian model to design Quantization Tables for DCT-based image coding. When applied to standard JPEG encoding, it provides more than 1.5-dB performance gain in PSNR, with almost no extra burden on complexity. Compared with the state-of-the-art JPEG Quantization Table optimizer, the proposed algorithm offers an average 0.5-dB gain in PSNR with computational complexity reduced by a factor of more than 2000 when SDQ is OFF, and a 0.2-dB performance gain or more with 85% of the complexity reduced when SDQ is ON. Significant compression performance improvement is also seen when the algorithm is applied to other image coding systems proposed in the literature.
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Quantization Table design revisited for image video coding
International Conference on Image Processing, 2013Co-Authors: Enhui Yang, Chang Sun, Jin MengAbstract:Quantization Table design is revisited for image/video coding where soft decision Quantization (SDQ) is considered. Unlike conventional approaches where Quantization Table design is bundled with a specific encoding method, we assume optimal SDQ encoding and design a Quantization Table for the purpose of reconstruction. Under this assumption, we model transform coefficients across different frequencies as independently distributed random sources and apply the Shannon lower bound to approximate the rate distortion function of each source. We then show that a Quantization Table can be optimized in a way that the resulting distortion complies with certain behavior. Lastly, guided by this new theoretical result, we propose an efficient statistical-model-based algorithm using the Laplacian model to design Quantization Tables for JPEG encoding. Compared with the state of the art, the proposed algorithm provides an average 0.5 dB gain in PSNR with computational complexity reduced by a factor of more than 2000 when SDQ is off, and a 0.1 dB performance gain with 85% of the complexity reduced when SDQ is on.
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ICIP - Quantization Table design revisited for image/video coding
2013 IEEE International Conference on Image Processing, 2013Co-Authors: Enhui Yang, Chang Sun, Jin MengAbstract:Quantization Table design is revisited for image/video coding where soft decision Quantization (SDQ) is considered. Unlike conventional approaches where Quantization Table design is bundled with a specific encoding method, we assume optimal SDQ encoding and design a Quantization Table for the purpose of reconstruction. Under this assumption, we model transform coefficients across different frequencies as independently distributed random sources and apply the Shannon lower bound to approximate the rate distortion function of each source. We then show that a Quantization Table can be optimized in a way that the resulting distortion complies with certain behavior. Lastly, guided by this new theoretical result, we propose an efficient statistical-model-based algorithm using the Laplacian model to design Quantization Tables for JPEG encoding. Compared with the state of the art, the proposed algorithm provides an average 0.5 dB gain in PSNR with computational complexity reduced by a factor of more than 2000 when SDQ is off, and a 0.1 dB performance gain with 85% of the complexity reduced when SDQ is on.
Chang Sun - One of the best experts on this subject based on the ideXlab platform.
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Quantization Table design revisited for image/video coding.
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 2014Co-Authors: Enhui Yang, Chang Sun, Jin MengAbstract:Quantization Table design is revisited for image/video coding where soft decision Quantization (SDQ) is considered. Unlike conventional approaches, where Quantization Table design is bundled with a specific encoding method, we assume optimal SDQ encoding and design a Quantization Table for the purpose of reconstruction. Under this assumption, we model transform coefficients across different frequencies as independently distributed random sources and apply the Shannon lower bound to approximate the rate distortion function of each source. We then show that a Quantization Table can be optimized in a way that the resulting distortion complies with certain behavior. Guided by this new design principle, we propose an efficient statistical-model-based algorithm using the Laplacian model to design Quantization Tables for DCT-based image coding. When applied to standard JPEG encoding, it provides more than 1.5-dB performance gain in PSNR, with almost no extra burden on complexity. Compared with the state-of-the-art JPEG Quantization Table optimizer, the proposed algorithm offers an average 0.5-dB gain in PSNR with computational complexity reduced by a factor of more than 2000 when SDQ is OFF, and a 0.2-dB performance gain or more with 85% of the complexity reduced when SDQ is ON. Significant compression performance improvement is also seen when the algorithm is applied to other image coding systems proposed in the literature.
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Quantization Table design revisited for image video coding
IEEE Transactions on Image Processing, 2014Co-Authors: Enhui Yang, Chang Sun, Jin MengAbstract:Quantization Table design is revisited for image/video coding where soft decision Quantization (SDQ) is considered. Unlike conventional approaches, where Quantization Table design is bundled with a specific encoding method, we assume optimal SDQ encoding and design a Quantization Table for the purpose of reconstruction. Under this assumption, we model transform coefficients across different frequencies as independently distributed random sources and apply the Shannon lower bound to approximate the rate distortion function of each source. We then show that a Quantization Table can be optimized in a way that the resulting distortion complies with certain behavior. Guided by this new design principle, we propose an efficient statistical-model-based algorithm using the Laplacian model to design Quantization Tables for DCT-based image coding. When applied to standard JPEG encoding, it provides more than 1.5-dB performance gain in PSNR, with almost no extra burden on complexity. Compared with the state-of-the-art JPEG Quantization Table optimizer, the proposed algorithm offers an average 0.5-dB gain in PSNR with computational complexity reduced by a factor of more than 2000 when SDQ is OFF, and a 0.2-dB performance gain or more with 85% of the complexity reduced when SDQ is ON. Significant compression performance improvement is also seen when the algorithm is applied to other image coding systems proposed in the literature.
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Quantization Table design revisited for image video coding
International Conference on Image Processing, 2013Co-Authors: Enhui Yang, Chang Sun, Jin MengAbstract:Quantization Table design is revisited for image/video coding where soft decision Quantization (SDQ) is considered. Unlike conventional approaches where Quantization Table design is bundled with a specific encoding method, we assume optimal SDQ encoding and design a Quantization Table for the purpose of reconstruction. Under this assumption, we model transform coefficients across different frequencies as independently distributed random sources and apply the Shannon lower bound to approximate the rate distortion function of each source. We then show that a Quantization Table can be optimized in a way that the resulting distortion complies with certain behavior. Lastly, guided by this new theoretical result, we propose an efficient statistical-model-based algorithm using the Laplacian model to design Quantization Tables for JPEG encoding. Compared with the state of the art, the proposed algorithm provides an average 0.5 dB gain in PSNR with computational complexity reduced by a factor of more than 2000 when SDQ is off, and a 0.1 dB performance gain with 85% of the complexity reduced when SDQ is on.
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ICIP - Quantization Table design revisited for image/video coding
2013 IEEE International Conference on Image Processing, 2013Co-Authors: Enhui Yang, Chang Sun, Jin MengAbstract:Quantization Table design is revisited for image/video coding where soft decision Quantization (SDQ) is considered. Unlike conventional approaches where Quantization Table design is bundled with a specific encoding method, we assume optimal SDQ encoding and design a Quantization Table for the purpose of reconstruction. Under this assumption, we model transform coefficients across different frequencies as independently distributed random sources and apply the Shannon lower bound to approximate the rate distortion function of each source. We then show that a Quantization Table can be optimized in a way that the resulting distortion complies with certain behavior. Lastly, guided by this new theoretical result, we propose an efficient statistical-model-based algorithm using the Laplacian model to design Quantization Tables for JPEG encoding. Compared with the state of the art, the proposed algorithm provides an average 0.5 dB gain in PSNR with computational complexity reduced by a factor of more than 2000 when SDQ is off, and a 0.1 dB performance gain with 85% of the complexity reduced when SDQ is on.
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dithered soft decision Quantization for baseline jpeg encoding and its joint optimization with huffman coding and Quantization Table selection
Asilomar Conference on Signals Systems and Computers, 2011Co-Authors: Enhui Yang, Chang SunAbstract:Based on baseline JPEG, a new image coding framework is first developed, where dithered (uniform) quantizers are used to replace JPEG uniform quantizers for the purpose of improving the rate-distortion performance without sacrificing the coding complexity instead of the conventional subjective image quality. By combining dithering with soft decision Quantization (SDQ)—yielding dithered SDQ, an iterative algorithm is then proposed for jointly designing dithers for DCT coefficients at each frequency (i.e., dither Table), Quantization Table, run-length coding, and Huffman coding. The algorithm converges in the sense that its rate-distortion cost is monotonically decreasing until a stationary point is reached. When compared with state-of-the-art baseline JPEG R-D optimizer proposed recently by Yang and Wang, our algorithm achieves comparable and sometimes better rate-distortion performance with 65% computational complexity reduction.
Long-wen Chang - One of the best experts on this subject based on the ideXlab platform.
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Designing JPEG Quantization Tables based on human visual system
Signal Processing: Image Communication, 2001Co-Authors: Ching Yang Wang, Shiuh Ming Lee, Long-wen ChangAbstract:In this paper, we propose a systematic procedure to design a Quantization Table based on the human visual system model for the baseline JPEG coder. By incorporating the human visual system model with a uniform quantizer, a perceptual Quantization Table is derived. The Quantization Table can be easily adapted to the specified resolution for viewing and printing. Experimental results indicate that the derived HVS-based Quantization Table can achieve better performance in rate-distortion sense than the JPEG default Quantization Table.
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ICIP (2) - Designing JPEG Quantization Tables based on human visual system
Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348), 1999Co-Authors: Long-wen Chang, Ching Yang Wang, Shiuh Ming LeeAbstract:In this paper, a Quantization Table based on the human visual system is designed for the baseline JPEG coder. By incorporating the human visual system with the uniform quantizer, a perceptual Quantization Table is derived. The Quantization Table is easy to adapt to the specified resolution for viewing and printing. Experimental results indicate that the HVS-based Quantization Table can achieve improvements in PSNR about 0.2-2.0 dB without increasing the complexity in both encoder and encoder.
