The Experts below are selected from a list of 198 Experts worldwide ranked by ideXlab platform
Bing Zeng - One of the best experts on this subject based on the ideXlab platform.
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Compression-Dependent Transform-Domain Downward Conversion for Block-Based Image Coding
IEEE Transactions on Image Processing, 2018Co-Authors: Zhiying He, Jiantao Zhou, Xiandong Meng, Bing ZengAbstract:Transform-domain downward conversion (TDDC) for image coding is usually implemented by discarding some high-frequency components from each transformed Block. As a result, a Block of fewer Coefficients is formed, and a lower compression cost is achieved due to the coding of only a few low-frequency Coefficients. In this paper, we focus on the design of a new TDDC-based coding method by using our proposed interpolation-compression directed filtering (ICDF) and error-compensated scalar quantization (ECSQ), leading to the compression-dependent TDDC (CDTDDC)-based coding. More specifically, ICDF is first used to convert each $16\times 16$ macro-Block into an $8\times 8$ Coefficient Block. Then, this Coefficient Block is compressed with ECSQ, resulting in a smaller compression distortion for those pixels that locate at some specific positions of a macro-Block. We select these positions according to the 4:1 uniform sub-sampling lattice and use the pixels locating at them to reconstruct the whole macro-Block through an interpolation. The proposed CDTDDC-based coding can be applied to compress both grayscale and color images. More importantly, when it is used in the color image compression, it offers not only a new solution to reduce the data-size of chrominance components but also a higher compression efficiency. Experimental results demonstrate that applying our proposed CDTDDC-based coding to compress still images can achieve a significant quality gain over the existing compression methods.
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Compression-Dependent Transform-Domain Downward Conversion for Block-Based Image Coding
IEEE Transactions on Image Processing, 2018Co-Authors: Zhiying He, Jiantao Zhou, Xiandong Meng, Bing ZengAbstract:Transform-domain downward conversion (TDDC) for image coding is usually implemented by discarding some high frequency components from each transformed Block. As a result, a Block of fewer Coefficients is formed, and a lower compression cost is achieved due to the coding of only a few low-frequency Coefficients. In this paper, we focus on the design of a new TDDC-based coding method by using our proposed interpolation compression directed filtering (ICDF) and error-compensated scalar quantization (ECSQ), leading to the compression dependent TDDC (CDTDDC)-based coding. More specifically, ICDF is first used to convert each 16 × 16 macro-Block into an 8 × 8 Coefficient Block. Then, this Coefficient Block is compressed with ECSQ, resulting in a smaller compression distortion for those pixels that locate at some specific positions of a macroBlock. We select these positions according to the 4:1 uniform sub-sampling lattice and use the pixels locating at them to reconstruct the whole macro-Block through an interpolation. The proposed CDTDDC-based coding can be applied to compress both grayscale and color images. More importantly, when it is used in the color image compression, it offers not only a new solution to reduce the data-size of chrominance components but also a higher compression efficiency. Experimental results demonstrate that applying our proposed CDTDDC-based coding to compress still images can achieve a significant quality gain over the existing compression methods.
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Block-based Image Coding by Compression-Constrained Transform Domain Down-Scaling
2018 IEEE Visual Communications and Image Processing (VCIP), 2018Co-Authors: Xiandong Meng, Bing ZengAbstract:Transform domain down-scaling (TDDS) is traditionally implemented by dropping most of high-frequency components of the transformed Block. Applying it to image compression can improve the compression efficiency by saving considerable bit-cost. Due to losing some necessary high-frequency information, the resulted image compressed by using the traditional TDDS-based coding often suffers a serious quality degradation. In this paper, we propose a compression-constrained TDDS and perform it on each N × N Block to produce an N/2 × N/2 Coefficient Block for the compression. Our proposed TDDS not only guarantees a high reconstruction quality but also makes a low bit-cost for compression. We integrate it in practical image coding to build up our proposed compression scheme. Experimental results show that our proposed method demonstrates excellent coding performance when used to compress image signals.
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VCIP - Interpolation-directed transform domain downward conversion for Block-based image compression
2016 Visual Communications and Image Processing (VCIP), 2016Co-Authors: Jinglin Yu, Mingyu Li, Chen Chen, Liaoyuan Zeng, Bing ZengAbstract:In this paper, we design an interpolation-directed transform domain downward conversion (ITDDC) to build up a new Block-based image compression scheme. This ITDDC is derived from our proposed 2-D padding and performed on each 16×16 macro-Block of pixels to convert it into an 8×8 Coefficient Block, leading to a downward image conversion in the transform domain. More interestingly, the further compression is just performed on the down-sized Coefficient Block and the reconstruction for an entire macro-Block is achieved via the interpolation by using the decoded pixels only locating in some specific positions of it. To make the interpolation more efficient, the pixels participating in the interpolation will be optimized before the compression. The ITDDC-based coding is used competitively with the JPEG baseline coding to compress each macro-Block in our proposed compression scheme according to a simple but efficient rate-distortion optimization based criterion. Experimental results demonstrate that our proposed method gets a remarkable quality gain over the existing approaches.
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Constrained quantization based transform domain down-conversion for image compression
2016 IEEE International Symposium on Circuits and Systems (ISCAS), 2016Co-Authors: Liaoyuan Zeng, Bing Zeng, Jiantao ZhouAbstract:The image down-conversion may be used in the Block-based image compression because it can help save lots of bit-counts for each individual Block. A straightforward way to implement the transform domain down-conversion is to truncate some high-frequency components to get a down-sized Coefficient Block. However, directly using this down-sized Coefficient Block to reconstruct a completed image Block will lead to a serious quality degradation. In this paper, we propose a constrained quantization based transform domain down-conversion (CQTDD) to help compress each 16×16 macro-Block and it makes the coding quality of 1/4 selected pixels (according to a regular pattern) in each macro-Block much higher than that can be achieved by using the traditional truncation based approach. Meanwhile, the other 3/4 pixels will be interpolated by using those 1/4 well-reconstructed pixels. Furthermore, these 1/4 pixels are optimized before the compression to help get a more efficient interpolation. Finally, the proposed CQTDD works with the JPEG baseline coding together as two candidate coding modes in our proposed compression scheme. Experimental results demonstrate that our proposed method may offer a remarkable quality gain, both objectively and subjectively, compared with some existing methods.
Jiantao Zhou - One of the best experts on this subject based on the ideXlab platform.
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Compression-Dependent Transform-Domain Downward Conversion for Block-Based Image Coding
IEEE Transactions on Image Processing, 2018Co-Authors: Zhiying He, Jiantao Zhou, Xiandong Meng, Bing ZengAbstract:Transform-domain downward conversion (TDDC) for image coding is usually implemented by discarding some high-frequency components from each transformed Block. As a result, a Block of fewer Coefficients is formed, and a lower compression cost is achieved due to the coding of only a few low-frequency Coefficients. In this paper, we focus on the design of a new TDDC-based coding method by using our proposed interpolation-compression directed filtering (ICDF) and error-compensated scalar quantization (ECSQ), leading to the compression-dependent TDDC (CDTDDC)-based coding. More specifically, ICDF is first used to convert each $16\times 16$ macro-Block into an $8\times 8$ Coefficient Block. Then, this Coefficient Block is compressed with ECSQ, resulting in a smaller compression distortion for those pixels that locate at some specific positions of a macro-Block. We select these positions according to the 4:1 uniform sub-sampling lattice and use the pixels locating at them to reconstruct the whole macro-Block through an interpolation. The proposed CDTDDC-based coding can be applied to compress both grayscale and color images. More importantly, when it is used in the color image compression, it offers not only a new solution to reduce the data-size of chrominance components but also a higher compression efficiency. Experimental results demonstrate that applying our proposed CDTDDC-based coding to compress still images can achieve a significant quality gain over the existing compression methods.
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Compression-Dependent Transform-Domain Downward Conversion for Block-Based Image Coding
IEEE Transactions on Image Processing, 2018Co-Authors: Zhiying He, Jiantao Zhou, Xiandong Meng, Bing ZengAbstract:Transform-domain downward conversion (TDDC) for image coding is usually implemented by discarding some high frequency components from each transformed Block. As a result, a Block of fewer Coefficients is formed, and a lower compression cost is achieved due to the coding of only a few low-frequency Coefficients. In this paper, we focus on the design of a new TDDC-based coding method by using our proposed interpolation compression directed filtering (ICDF) and error-compensated scalar quantization (ECSQ), leading to the compression dependent TDDC (CDTDDC)-based coding. More specifically, ICDF is first used to convert each 16 × 16 macro-Block into an 8 × 8 Coefficient Block. Then, this Coefficient Block is compressed with ECSQ, resulting in a smaller compression distortion for those pixels that locate at some specific positions of a macroBlock. We select these positions according to the 4:1 uniform sub-sampling lattice and use the pixels locating at them to reconstruct the whole macro-Block through an interpolation. The proposed CDTDDC-based coding can be applied to compress both grayscale and color images. More importantly, when it is used in the color image compression, it offers not only a new solution to reduce the data-size of chrominance components but also a higher compression efficiency. Experimental results demonstrate that applying our proposed CDTDDC-based coding to compress still images can achieve a significant quality gain over the existing compression methods.
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Constrained quantization based transform domain down-conversion for image compression
2016 IEEE International Symposium on Circuits and Systems (ISCAS), 2016Co-Authors: Liaoyuan Zeng, Bing Zeng, Jiantao ZhouAbstract:The image down-conversion may be used in the Block-based image compression because it can help save lots of bit-counts for each individual Block. A straightforward way to implement the transform domain down-conversion is to truncate some high-frequency components to get a down-sized Coefficient Block. However, directly using this down-sized Coefficient Block to reconstruct a completed image Block will lead to a serious quality degradation. In this paper, we propose a constrained quantization based transform domain down-conversion (CQTDD) to help compress each 16×16 macro-Block and it makes the coding quality of 1/4 selected pixels (according to a regular pattern) in each macro-Block much higher than that can be achieved by using the traditional truncation based approach. Meanwhile, the other 3/4 pixels will be interpolated by using those 1/4 well-reconstructed pixels. Furthermore, these 1/4 pixels are optimized before the compression to help get a more efficient interpolation. Finally, the proposed CQTDD works with the JPEG baseline coding together as two candidate coding modes in our proposed compression scheme. Experimental results demonstrate that our proposed method may offer a remarkable quality gain, both objectively and subjectively, compared with some existing methods.
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ISCAS - Constrained quantization based transform domain down-conversion for image compression
2016 IEEE International Symposium on Circuits and Systems (ISCAS), 2016Co-Authors: Liaoyuan Zeng, Bing Zeng, Jiantao ZhouAbstract:The image down-conversion may be used in the Block-based image compression because it can help save lots of bit-counts for each individual Block. A straightforward way to implement the transform domain down-conversion is to truncate some high-frequency components to get a down-sized Coefficient Block. However, directly using this down-sized Coefficient Block to reconstruct a completed image Block will lead to a serious quality degradation. In this paper, we propose a constrained quantization based transform domain down-conversion (CQTDD) to help compress each 16×16 macro-Block and it makes the coding quality of 1/4 selected pixels (according to a regular pattern) in each macro-Block much higher than that can be achieved by using the traditional truncation based approach. Meanwhile, the other 3/4 pixels will be interpolated by using those 1/4 well-reconstructed pixels. Furthermore, these 1/4 pixels are optimized before the compression to help get a more efficient interpolation. Finally, the proposed CQTDD works with the JPEG baseline coding together as two candidate coding modes in our proposed compression scheme. Experimental results demonstrate that our proposed method may offer a remarkable quality gain, both objectively and subjectively, compared with some existing methods.
Liaoyuan Zeng - One of the best experts on this subject based on the ideXlab platform.
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VCIP - Interpolation-directed transform domain downward conversion for Block-based image compression
2016 Visual Communications and Image Processing (VCIP), 2016Co-Authors: Jinglin Yu, Mingyu Li, Chen Chen, Liaoyuan Zeng, Bing ZengAbstract:In this paper, we design an interpolation-directed transform domain downward conversion (ITDDC) to build up a new Block-based image compression scheme. This ITDDC is derived from our proposed 2-D padding and performed on each 16×16 macro-Block of pixels to convert it into an 8×8 Coefficient Block, leading to a downward image conversion in the transform domain. More interestingly, the further compression is just performed on the down-sized Coefficient Block and the reconstruction for an entire macro-Block is achieved via the interpolation by using the decoded pixels only locating in some specific positions of it. To make the interpolation more efficient, the pixels participating in the interpolation will be optimized before the compression. The ITDDC-based coding is used competitively with the JPEG baseline coding to compress each macro-Block in our proposed compression scheme according to a simple but efficient rate-distortion optimization based criterion. Experimental results demonstrate that our proposed method gets a remarkable quality gain over the existing approaches.
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Constrained quantization based transform domain down-conversion for image compression
2016 IEEE International Symposium on Circuits and Systems (ISCAS), 2016Co-Authors: Liaoyuan Zeng, Bing Zeng, Jiantao ZhouAbstract:The image down-conversion may be used in the Block-based image compression because it can help save lots of bit-counts for each individual Block. A straightforward way to implement the transform domain down-conversion is to truncate some high-frequency components to get a down-sized Coefficient Block. However, directly using this down-sized Coefficient Block to reconstruct a completed image Block will lead to a serious quality degradation. In this paper, we propose a constrained quantization based transform domain down-conversion (CQTDD) to help compress each 16×16 macro-Block and it makes the coding quality of 1/4 selected pixels (according to a regular pattern) in each macro-Block much higher than that can be achieved by using the traditional truncation based approach. Meanwhile, the other 3/4 pixels will be interpolated by using those 1/4 well-reconstructed pixels. Furthermore, these 1/4 pixels are optimized before the compression to help get a more efficient interpolation. Finally, the proposed CQTDD works with the JPEG baseline coding together as two candidate coding modes in our proposed compression scheme. Experimental results demonstrate that our proposed method may offer a remarkable quality gain, both objectively and subjectively, compared with some existing methods.
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ISCAS - Constrained quantization based transform domain down-conversion for image compression
2016 IEEE International Symposium on Circuits and Systems (ISCAS), 2016Co-Authors: Liaoyuan Zeng, Bing Zeng, Jiantao ZhouAbstract:The image down-conversion may be used in the Block-based image compression because it can help save lots of bit-counts for each individual Block. A straightforward way to implement the transform domain down-conversion is to truncate some high-frequency components to get a down-sized Coefficient Block. However, directly using this down-sized Coefficient Block to reconstruct a completed image Block will lead to a serious quality degradation. In this paper, we propose a constrained quantization based transform domain down-conversion (CQTDD) to help compress each 16×16 macro-Block and it makes the coding quality of 1/4 selected pixels (according to a regular pattern) in each macro-Block much higher than that can be achieved by using the traditional truncation based approach. Meanwhile, the other 3/4 pixels will be interpolated by using those 1/4 well-reconstructed pixels. Furthermore, these 1/4 pixels are optimized before the compression to help get a more efficient interpolation. Finally, the proposed CQTDD works with the JPEG baseline coding together as two candidate coding modes in our proposed compression scheme. Experimental results demonstrate that our proposed method may offer a remarkable quality gain, both objectively and subjectively, compared with some existing methods.
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Interpolation-directed transform domain downward conversion for Block-based image compression
2016 Visual Communications and Image Processing (VCIP), 2016Co-Authors: Jinglin Yu, Mingyu Li, Chen Chen, Liaoyuan Zeng, Bing ZengAbstract:In this paper, we design an interpolation-directed transform domain downward conversion (ITDDC) to build up a new Block-based image compression scheme. This ITDDC is derived from our proposed 2-D padding and performed on each 16×16 macro-Block of pixels to convert it into an 8×8 Coefficient Block, leading to a downward image conversion in the transform domain. More interestingly, the further compression is just performed on the down-sized Coefficient Block and the reconstruction for an entire macro-Block is achieved via the interpolation by using the decoded pixels only locating in some specific positions of it. To make the interpolation more efficient, the pixels participating in the interpolation will be optimized before the compression. The ITDDC-based coding is used competitively with the JPEG baseline coding to compress each macro-Block in our proposed compression scheme according to a simple but efficient rate-distortion optimization based criterion. Experimental results demonstrate that our proposed method gets a remarkable quality gain over the existing approaches.
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Image compression based on the transform domain downward conversion
2015 IEEE International Conference on Digital Signal Processing (DSP), 2015Co-Authors: Zexiang Miao, Bing Zeng, Liaoyuan ZengAbstract:In this paper, we focus on the design of a new Block-based image compression by using our proposed transform domain downward conversion (TDDC). Applied directly on each 16×16 macro-Block of pixels, this downward conversion is implemented through our proposed advanced padding technique such that a non-zero 8×8 Coefficient Block (thus down-sized) is generated only at the top-left corner in the transform domain, accompanied by zeros in other 75% positions. Consequently, a considerable bit-count saving can be achieved for the whole macro-Block. In the meantime, 25% pixels reserved during the TDDC may be directly reconstructed from the down-sized Coefficient Block while the other 75% pixels that are not reserved during the TDDC will be reconstructed via the interpolation. Finally, this TDDC-based compression is used in conjunction with the JPEG baseline coding method (i.e., 1-out-of-2 selection) according to a rate-distortion optimization (RDO) based criterion. Experimental results show that our proposed compression scheme provides a significant quality gain as compared with the original JPEG baseline coding method and another super-resolution directed down-sampling (SRDDS) based compression scheme.
Dongsheng Wang - One of the best experts on this subject based on the ideXlab platform.
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ICIP - Binary classification based linear rate estimation model for HEVC RDO
2014 IEEE International Conference on Image Processing (ICIP), 2014Co-Authors: Dongsheng WangAbstract:Rate-Distortion Optimization in High Efficiency Video Coding promotes the coding efficiency, but also imposes intensive computation to the encoder, because the complex Syntax-based context-adaptive Binary Arithmetic Coding is performed for each candidate coding configuration. We develop the classification based regression method to derive the rate models, which fast estimate the bit cost of quantization Coefficient Block from its distribution features. Experiments demonstrate that, our method reduces the averaged 28.4% computation time in rate cost estimation, while the coding efficiency degradation is 0.0428dB.
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Linear Rate Estimation Model for HEVC RDO Using Binary Classification Based Regression
2014 Data Compression Conference, 2014Co-Authors: Dongsheng Wang, Yang SongAbstract:Rate-Distortion Optimization in High Efficiency Video Coding promotes the coding efficiency, but also imposes intensive computation to the encoder, because the complex Syntax-based context-adaptive Binary Arithmetic Coding is performed for each candidate coding configuration. We develop the classification based regression method to derive the rate models, which fast estimate the bit cost of quantization Coefficient Block from its distribution features. Experiments demonstrate that, our method reduces the averaged 28.4% computation time in rate cost estimation, while the coding efficiency degradation is 0.0428dB.
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DCC - Linear Rate Estimation Model for HEVC RDO Using Binary Classification Based Regression
2014 Data Compression Conference, 2014Co-Authors: Dongsheng Wang, Yang SongAbstract:Rate-Distortion Optimization in High Efficiency Video Coding promotes the coding efficiency, but also imposes intensive computation to the encoder, because the complex Syntax-based context-adaptive Binary Arithmetic Coding is performed for each candidate coding configuration. We develop the classification based regression method to derive the rate models, which fast estimate the bit cost of quantization Coefficient Block from its distribution features. Experiments demonstrate that, our method reduces the averaged 28.4% computation time in rate cost estimation, while the coding efficiency degradation is 0.0428dB.
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Binary classification based linear rate estimation model for HEVC RDO
2014 IEEE International Conference on Image Processing (ICIP), 2014Co-Authors: Dongsheng WangAbstract:Rate-Distortion Optimization in High Efficiency Video Coding promotes the coding efficiency, but also imposes intensive computation to the encoder, because the complex Syntax-based context-adaptive Binary Arithmetic Coding is performed for each candidate coding configuration. We develop the classification based regression method to derive the rate models, which fast estimate the bit cost of quantization Coefficient Block from its distribution features. Experiments demonstrate that, our method reduces the averaged 28.4% computation time in rate cost estimation, while the coding efficiency degradation is 0.0428dB.
Yang Song - One of the best experts on this subject based on the ideXlab platform.
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Linear Rate Estimation Model for HEVC RDO Using Binary Classification Based Regression
2014 Data Compression Conference, 2014Co-Authors: Dongsheng Wang, Yang SongAbstract:Rate-Distortion Optimization in High Efficiency Video Coding promotes the coding efficiency, but also imposes intensive computation to the encoder, because the complex Syntax-based context-adaptive Binary Arithmetic Coding is performed for each candidate coding configuration. We develop the classification based regression method to derive the rate models, which fast estimate the bit cost of quantization Coefficient Block from its distribution features. Experiments demonstrate that, our method reduces the averaged 28.4% computation time in rate cost estimation, while the coding efficiency degradation is 0.0428dB.
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DCC - Linear Rate Estimation Model for HEVC RDO Using Binary Classification Based Regression
2014 Data Compression Conference, 2014Co-Authors: Dongsheng Wang, Yang SongAbstract:Rate-Distortion Optimization in High Efficiency Video Coding promotes the coding efficiency, but also imposes intensive computation to the encoder, because the complex Syntax-based context-adaptive Binary Arithmetic Coding is performed for each candidate coding configuration. We develop the classification based regression method to derive the rate models, which fast estimate the bit cost of quantization Coefficient Block from its distribution features. Experiments demonstrate that, our method reduces the averaged 28.4% computation time in rate cost estimation, while the coding efficiency degradation is 0.0428dB.