The Experts below are selected from a list of 15093 Experts worldwide ranked by ideXlab platform
Ya-qin Zhang - One of the best experts on this subject based on the ideXlab platform.
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macroblock based progressive Fine Granularity scalable pfgs video coding with flexible temporal snr scalablilities
International Conference on Image Processing, 2001Co-Authors: Xiaoyan Sun, Wen Gao, Ya-qin ZhangAbstract:We proposed a flexible and efficient architecture for scalable video coding, namely, the macroblock (MB)-based progressive Fine Granularity scalable video coding with temporal-SNR scalabilities (PFGST). The proposed architecture can provide not only much improved coding efficiency but also simultaneous SNR scalability and temporal scalability. Building upon the original frame-based progressive Fine Granularity scalable (PFGS) coding approach, the MB-based PFGS scheme is first proposed. Three INTER modes and the corresponding mode selection mechanism are presented for coding the SNR enhancement MBs in order to make a good trade-off between low drifting errors and high compression efficiency. Furthermore, temporal scalability is introduced into the MB-based PFGS, which forms the MB-based PFGST scheme. Two coding modes are proposed for coding the temporal enhancement MBs. Since it would not cause any error propagation if using the high quality reference in the temporal enhancement MB coding, the coding efficiency of the PFGST is highly improved by always choosing the most suitable reference for the temporal scalable coding. Experimental results show that the MB-based PFGST video coding scheme can significantly improve the coding efficiency up to 2.8 dB compared with the FGST scheme adopted in MPEG-4, while supporting full SNR, full temporal, and hybrid SNR-temporal scalabilities according to the different requirements from the channels, the clients or the servers.
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macroblock based progressive Fine Granularity scalable video coding
International Conference on Multimedia and Expo, 2001Co-Authors: Xiaoyan Sun, Wen Gao, Ya-qin ZhangAbstract:The Progressive Fine Granularity Scalable (PFGS) coding is a promising technique for streaming video applications. However, since the original PFGS only chooses its references as framebased, it is very difficult to achieve a good trade-off between high coding efficiency and low drifting errors. In this paper, we present a flexible and effective scheme to control the PFGS coding at the macroblock level. Three INTER modes are first proposed for the enhancement macroblock coding. One of these modes provides a novel method to effectively reduce the drifting errors at low bit rates. Then, a decision-making mechanism based on temporal prediction is developed to choose the optimal coding mode for each enhancement macroblock, which offers a significant coding efficiency improvement for the Fine Granularity scalable coding scheme. Moreover, the proposed control mechanism can be easily implemented without any additional computation. The experimental results show that the proposed macroblock-based PFGS coding scheme can effectively reduce the drifting errors at low bit rates, while providing further coding efficiency improvement over the FGS and the original PFGS scheme at moderate or high bit rates.
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A framework for efficient progressive Fine Granularity scalable video coding
IEEE Transactions on Circuits and Systems for Video Technology, 2001Co-Authors: Ya-qin ZhangAbstract:A basic framework for efficient scalable video coding, namely progressive Fine Granularity scalable (PFGS) video coding is proposed. Similar to the Fine Granularity scalable (PGS) video coding in MPEG-4, the PFGS framework has all the features of FGS, such as Fine Granularity bit-rate scalability, channel adaptation, and error recovery. On the other hand, different from the PGS coding, the PFGS framework uses multiple layers of references with increasing quality to make motion prediction more accurate for improved video-coding efficiency. However, using multiple layers of references with different quality also introduces several issues. First, extra frame buffers are needed for storing the multiple reconstructed reference layers. This would increase the memory cost and computational complexity of the PFGS scheme. Based on the basic framework, a simplified and efficient PFGS framework is further proposed. The simplified PPGS framework needs only one extra frame buffer with almost the same coding efficiency as in the original framework. Second, there might be undesirable increase and fluctuation of the coefficients to be coded when switching from a low-quality reference to a high-quality one, which could partially offset the advantage of using a high-quality reference. A further improved PFGS scheme can eliminate the fluctuation of enhancement-layer coefficients when switching references by always using only one high-quality prediction reference for all enhancement layers. Experimental results show that the PFGS framework can improve the coding efficiency up to more than 1 dB over the FGS scheme in terms of average PSNR, yet still keeps all the original properties, such as Fine Granularity, bandwidth adaptation, and error recovery. A simple simulation of transmitting the PFGS video over a wireless channel further confirms the error robustness of the PFGS scheme, although the advantages of PFGS have not been fully exploited.
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ICME - Macroblock-based progressive Fine Granularity scalable video coding
IEEE International Conference on Multimedia and Expo 2001. ICME 2001., 2001Co-Authors: Xiaoyan Sun, Wen Gao, Ya-qin ZhangAbstract:The Progressive Fine Granularity Scalable (PFGS) coding is a promising technique for streaming video applications. However, since the original PFGS only chooses its references as framebased, it is very difficult to achieve a good trade-off between high coding efficiency and low drifting errors. In this paper, we present a flexible and effective scheme to control the PFGS coding at the macroblock level. Three INTER modes are first proposed for the enhancement macroblock coding. One of these modes provides a novel method to effectively reduce the drifting errors at low bit rates. Then, a decision-making mechanism based on temporal prediction is developed to choose the optimal coding mode for each enhancement macroblock, which offers a significant coding efficiency improvement for the Fine Granularity scalable coding scheme. Moreover, the proposed control mechanism can be easily implemented without any additional computation. The experimental results show that the proposed macroblock-based PFGS coding scheme can effectively reduce the drifting errors at low bit rates, while providing further coding efficiency improvement over the FGS and the original PFGS scheme at moderate or high bit rates.
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ICASSP - Fine-Granularity spatially scalable video coding
2001 IEEE International Conference on Acoustics Speech and Signal Processing. Proceedings (Cat. No.01CH37221), 1Co-Authors: Qi Wang, Yuzhuo Zhong, Ya-qin ZhangAbstract:We propose a novel architecture for spatially scalable video coding, namely, Fine-Granularity spatially scalable (FGSS) coding. The traditional layered spatially scalable coding provides only coarse scalability in which the bit-stream can be decoded only at a few fixed resolutions, but not something in between. The proposed FGSS scheme provides a Fine-Granularity property to the spatial scalability. In this scheme, the bit plane technique is combined with spatial scalability, thus a Fine Granularity increase in the image quality from low-resolution to high-resolution can be obtained. In addition, the proposed scheme provides a flexible embedded bitstream that can be decoded up to any point in the enhancement layer bitstream from low-resolution to high-resolution. This feature further enables efficient video streaming over the Internet where the scalable bitstream, can adapt to the widely fluctuating bandwidth. The FGSS coding scheme extends new functionalities such as multi-resolution, Fine Granularity, channel adaptation and error-recovery properties to scalable video coding, thus it can satisfy different user clients with a wide range of channel bandwidth and screen resolution.
Tihao Chiang - One of the best experts on this subject based on the ideXlab platform.
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Stack robust Fine Granularity scalable video coding
Journal of the Chinese Institute of Engineers, 2006Co-Authors: Hsiangchun Huang, Tihao ChiangAbstract:Abstract A novel scalable video coding technique, namely Stack Robust Fine Granularity Scalability (SRFGS), is presented to provide both temporal and SNR scalability. The SRFGS first simplifies the temporal prediction architecture of RFGS. The approach is further generalized using a reconstructed frame from the previous time instance of the same layer to temporally predict the quantization error of the lower layer. With this concept, the RFGS architecture can be extended to multi‐layer stack architecture. The SRFGS can be optimized at several operating points to meet the requirements of various applications, while maintaining the Fine Granularity and error robustness of RFGS. An optimized macroblock‐based alpha adaptation scheme is proposed to improve the coding efficiency. A single‐loop enhancement layer decoding scheme is proposed to reduce the decoder complexity. The simulation results show that SRFGS can improve the performance of RFGS by 0.4 to 3.0 dB in PSNR. SRFGS has been reviewed by the MPEG comm...
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stack robust Fine Granularity scalability
International Symposium on Circuits and Systems, 2004Co-Authors: Hsiangchun Huang, Tihao ChiangAbstract:A scalable video coding technique, named as stack robust Fine Granularity scalability (SRFGS), is presented to provide simultaneously temporal scalability and SNR scalability. SRFGS first simplifies the RFGS as stated in H. C. Huang et al. (2002) temporal prediction architecture and then generalizes the prediction concept as following: the quantization error of the previous layer can be inter-predicted by the reconstructed frame in the previous time instance of the same layer. With this concept, the RFGS architecture can be extended to multiple layers, which form the stack architecture, SRFGS can be optimized at several operating points to fit the requirement of various applications, while still maintaining the Fine Granularity and error robustness of RFGS. Thus, the stack prediction of SRFGS can improve the temporal prediction efficiency of RFGS. The simulation results show that SRFGS can improve the performance of RFGS by 0.5 to 3.0 dB in PSNR. In addition, SRFGS has been submitted to MPEG committee according to H.C. Huang et al. (2003) and ranked as one of the best algorithms according to the subjective testing in the Report on Call for Evidence on Scalable Video Coding (2003).
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a robust Fine Granularity scalability using trellis based predictive leak
International Symposium on Circuits and Systems, 2002Co-Authors: Hsiangchun Huang, Chungneng Wang, Tihao ChiangAbstract:Recently, the MPEG-4 committee has approved the MPEG-4 Streaming Video Profile (SVP) that includes the Fine Granularity Scalability (FGS) as a new coding tool. In this paper, we propose novel techniques to further improve the temporal prediction schemes at the enhancement layer so that both the coding efficiency and error resilience are superior to the existing FGS while the Fine Granularity scalability is preserved. Our approach utilizes two parameters, the number of bitplanes /spl beta/(0/spl les/ /spl beta//spl les/maximal number of bitplanes) and the amount of predictive leak /spl alpha/ (0/spl les//spl alpha//spl les/1), to control the construction of the reference frame at the enhancement layer. These parameters /spl alpha/ and /spl beta/ can be temporally selected to provide tradeoffs between coding efficiency, error propagation and the predictive drift. It also encompasses several well-known FGS techniques as special cases for particular sets of /spl alpha/ and /spl beta/. The experimental results show over 2 dB improvement in coding efficiency using the MPEG-4 testing conditions.
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a robust Fine Granularity scalability using trellis based predictive leak
IEEE Transactions on Circuits and Systems for Video Technology, 2002Co-Authors: Hsiangchun Huang, Chungneng Wang, Tihao ChiangAbstract:Recently, the MPEG-4 committee has approved the MPEG-4 Fine Granularity scalability (FGS) profile as a streaming video tool. We propose novel techniques to improve further the temporal prediction at the enhancement layer so that coding efficiency is superior to the existing FGS. Our approach utilizes two parameters, the number of bitplanes, /spl beta/ (0/spl les//spl beta//spl les/maximal number of bitplanes), and the amount of predictive leak, /spl alpha/ (0/spl les//spl alpha//spl les/1), to control the construction of the reference frame at the enhancement layer. Parameters /spl alpha/ and /spl beta/ can be selected for each frame to provide tradeoffs between coding efficiency and error drift. Our approach offers a general and flexible framework that allows further optimization. It also includes several well-known motion-compensated FGS techniques as special cases with particular sets of /spl alpha/ and /spl beta/. We analyze the theoretical advantages when /spl alpha/ and /spl beta/ are used, and provide an adaptive technique to select /spl alpha/ and /spl beta/, which yields an improved performance as compared to that of fixed parameters. An identical technique is applied to the base layer for further improvement. Our experimental results show over 4 dB improvements in coding efficiency using the MPEG-4 testing conditions. Removal of error propagation is demonstrated with several typical channel transmission scenarios.
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ISCAS (3) - Stack robust Fine Granularity scalability
2004 IEEE International Symposium on Circuits and Systems (IEEE Cat. No.04CH37512), 1Co-Authors: Hsiangchun Huang, Tihao ChiangAbstract:A scalable video coding technique, named as stack robust Fine Granularity scalability (SRFGS), is presented to provide simultaneously temporal scalability and SNR scalability. SRFGS first simplifies the RFGS as stated in H. C. Huang et al. (2002) temporal prediction architecture and then generalizes the prediction concept as following: the quantization error of the previous layer can be inter-predicted by the reconstructed frame in the previous time instance of the same layer. With this concept, the RFGS architecture can be extended to multiple layers, which form the stack architecture, SRFGS can be optimized at several operating points to fit the requirement of various applications, while still maintaining the Fine Granularity and error robustness of RFGS. Thus, the stack prediction of SRFGS can improve the temporal prediction efficiency of RFGS. The simulation results show that SRFGS can improve the performance of RFGS by 0.5 to 3.0 dB in PSNR. In addition, SRFGS has been submitted to MPEG committee according to H.C. Huang et al. (2003) and ranked as one of the best algorithms according to the subjective testing in the Report on Call for Evidence on Scalable Video Coding (2003).
Hsiangchun Huang - One of the best experts on this subject based on the ideXlab platform.
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Stack robust Fine Granularity scalable video coding
Journal of the Chinese Institute of Engineers, 2006Co-Authors: Hsiangchun Huang, Tihao ChiangAbstract:Abstract A novel scalable video coding technique, namely Stack Robust Fine Granularity Scalability (SRFGS), is presented to provide both temporal and SNR scalability. The SRFGS first simplifies the temporal prediction architecture of RFGS. The approach is further generalized using a reconstructed frame from the previous time instance of the same layer to temporally predict the quantization error of the lower layer. With this concept, the RFGS architecture can be extended to multi‐layer stack architecture. The SRFGS can be optimized at several operating points to meet the requirements of various applications, while maintaining the Fine Granularity and error robustness of RFGS. An optimized macroblock‐based alpha adaptation scheme is proposed to improve the coding efficiency. A single‐loop enhancement layer decoding scheme is proposed to reduce the decoder complexity. The simulation results show that SRFGS can improve the performance of RFGS by 0.4 to 3.0 dB in PSNR. SRFGS has been reviewed by the MPEG comm...
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stack robust Fine Granularity scalability
International Symposium on Circuits and Systems, 2004Co-Authors: Hsiangchun Huang, Tihao ChiangAbstract:A scalable video coding technique, named as stack robust Fine Granularity scalability (SRFGS), is presented to provide simultaneously temporal scalability and SNR scalability. SRFGS first simplifies the RFGS as stated in H. C. Huang et al. (2002) temporal prediction architecture and then generalizes the prediction concept as following: the quantization error of the previous layer can be inter-predicted by the reconstructed frame in the previous time instance of the same layer. With this concept, the RFGS architecture can be extended to multiple layers, which form the stack architecture, SRFGS can be optimized at several operating points to fit the requirement of various applications, while still maintaining the Fine Granularity and error robustness of RFGS. Thus, the stack prediction of SRFGS can improve the temporal prediction efficiency of RFGS. The simulation results show that SRFGS can improve the performance of RFGS by 0.5 to 3.0 dB in PSNR. In addition, SRFGS has been submitted to MPEG committee according to H.C. Huang et al. (2003) and ranked as one of the best algorithms according to the subjective testing in the Report on Call for Evidence on Scalable Video Coding (2003).
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a robust Fine Granularity scalability using trellis based predictive leak
International Symposium on Circuits and Systems, 2002Co-Authors: Hsiangchun Huang, Chungneng Wang, Tihao ChiangAbstract:Recently, the MPEG-4 committee has approved the MPEG-4 Streaming Video Profile (SVP) that includes the Fine Granularity Scalability (FGS) as a new coding tool. In this paper, we propose novel techniques to further improve the temporal prediction schemes at the enhancement layer so that both the coding efficiency and error resilience are superior to the existing FGS while the Fine Granularity scalability is preserved. Our approach utilizes two parameters, the number of bitplanes /spl beta/(0/spl les/ /spl beta//spl les/maximal number of bitplanes) and the amount of predictive leak /spl alpha/ (0/spl les//spl alpha//spl les/1), to control the construction of the reference frame at the enhancement layer. These parameters /spl alpha/ and /spl beta/ can be temporally selected to provide tradeoffs between coding efficiency, error propagation and the predictive drift. It also encompasses several well-known FGS techniques as special cases for particular sets of /spl alpha/ and /spl beta/. The experimental results show over 2 dB improvement in coding efficiency using the MPEG-4 testing conditions.
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a robust Fine Granularity scalability using trellis based predictive leak
IEEE Transactions on Circuits and Systems for Video Technology, 2002Co-Authors: Hsiangchun Huang, Chungneng Wang, Tihao ChiangAbstract:Recently, the MPEG-4 committee has approved the MPEG-4 Fine Granularity scalability (FGS) profile as a streaming video tool. We propose novel techniques to improve further the temporal prediction at the enhancement layer so that coding efficiency is superior to the existing FGS. Our approach utilizes two parameters, the number of bitplanes, /spl beta/ (0/spl les//spl beta//spl les/maximal number of bitplanes), and the amount of predictive leak, /spl alpha/ (0/spl les//spl alpha//spl les/1), to control the construction of the reference frame at the enhancement layer. Parameters /spl alpha/ and /spl beta/ can be selected for each frame to provide tradeoffs between coding efficiency and error drift. Our approach offers a general and flexible framework that allows further optimization. It also includes several well-known motion-compensated FGS techniques as special cases with particular sets of /spl alpha/ and /spl beta/. We analyze the theoretical advantages when /spl alpha/ and /spl beta/ are used, and provide an adaptive technique to select /spl alpha/ and /spl beta/, which yields an improved performance as compared to that of fixed parameters. An identical technique is applied to the base layer for further improvement. Our experimental results show over 4 dB improvements in coding efficiency using the MPEG-4 testing conditions. Removal of error propagation is demonstrated with several typical channel transmission scenarios.
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ISCAS (3) - Stack robust Fine Granularity scalability
2004 IEEE International Symposium on Circuits and Systems (IEEE Cat. No.04CH37512), 1Co-Authors: Hsiangchun Huang, Tihao ChiangAbstract:A scalable video coding technique, named as stack robust Fine Granularity scalability (SRFGS), is presented to provide simultaneously temporal scalability and SNR scalability. SRFGS first simplifies the RFGS as stated in H. C. Huang et al. (2002) temporal prediction architecture and then generalizes the prediction concept as following: the quantization error of the previous layer can be inter-predicted by the reconstructed frame in the previous time instance of the same layer. With this concept, the RFGS architecture can be extended to multiple layers, which form the stack architecture, SRFGS can be optimized at several operating points to fit the requirement of various applications, while still maintaining the Fine Granularity and error robustness of RFGS. Thus, the stack prediction of SRFGS can improve the temporal prediction efficiency of RFGS. The simulation results show that SRFGS can improve the performance of RFGS by 0.5 to 3.0 dB in PSNR. In addition, SRFGS has been submitted to MPEG committee according to H.C. Huang et al. (2003) and ranked as one of the best algorithms according to the subjective testing in the Report on Call for Evidence on Scalable Video Coding (2003).
Wang Xiang - One of the best experts on this subject based on the ideXlab platform.
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Research on Wireless Channel Fine Granularity Scalability Video Coding
Computer Science, 2006Co-Authors: Wang XiangAbstract:With the development of Internet and wireless communication,the video scalable coding technique has been thought of as the important study field.This paper fistly provides an overview of the Fine Granularity scalabiliaty(FGS) video coding technique in the Amendment of the MPEG-4.In addition,the research progress on Fine Granularity scal- ability video coding for wireless channel is analyzed.Finally,the state of arts and possible new directions of FGS are stated.
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Research Progress on Fine Granularity Scalability Video Coding
Computer Science, 2006Co-Authors: Wang XiangAbstract:With the development of Internet,more and more applications start to use the media streaming . The video scalable coding technique,one of core technology in media streaming,have been thought as the important study field. This paper fist provides an overview of the Fine Granularity scalabiliaty(FGS)video coding technique in the Amendment of the MPEG-4,and discusses the several extensible features of FGS coding and its defects. In addition,the research progress on Fine Granularity scalability video coding for Internet is analyzed. Finally,the state of arts and possible new directions of FGS are stated.
Qi Wang - One of the best experts on this subject based on the ideXlab platform.
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optimal rate allocation for progressive Fine Granularity scalable video coding
IEEE Signal Processing Letters, 2002Co-Authors: Qi Wang, Zixiang XiongAbstract:We examine the enhancement-layer rate allocation problem in progressive Fine Granularity scalable (PFGS) video coding. The problem arises from the fact that different frames in the enhancement layer have different rates in PFGS coding. A rate-distortion (R-D) function for a multiframe group is first established for enhancement-layer PFGS coding, followed by experiments to verify its validity using real test sequences. Optimal rate allocation among frames in the group is then given based on the R-D function, together with a simple implementation that is suitable for applications such as streaming video. Experiments show that, compared with uniform bit allocation, optimal bit allocation not only makes the quality variation in decoded video much smoother, but also improves the average PSNR of PFGS coding by 0.3-0.5 dB.
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optimal rate allocation for progressive Fine Granularity scalable video coding
International Conference on Information Technology: Coding and Computing, 2001Co-Authors: Qi Wang, Zixiang XiongAbstract:We examine the enhancement-layer rate allocation problem in progressive Fine Granularity scalable (PFGS) video coding. The problem arises from the fact that different frames in the enhancement layer have different rates in PFGS coding. A rate-distortion (R-D) function for a multi-frame group is first established for enhancement-layer PFGS coding, followed by experiments to verify its validity using real test sequences. Optimal rate allocation among frames in the group is then given based on the R-D function, together with a simple implementation that is suitable for applications like streaming video. Experiments show that, compared with uniform bit allocation, optimal bit allocation not only makes the quality variation in decoded video much smoother, but also improves the average PSNR of PFGS coding by 0.3-0.5 dB.
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ICASSP - Fine-Granularity spatially scalable video coding
2001 IEEE International Conference on Acoustics Speech and Signal Processing. Proceedings (Cat. No.01CH37221), 1Co-Authors: Qi Wang, Yuzhuo Zhong, Ya-qin ZhangAbstract:We propose a novel architecture for spatially scalable video coding, namely, Fine-Granularity spatially scalable (FGSS) coding. The traditional layered spatially scalable coding provides only coarse scalability in which the bit-stream can be decoded only at a few fixed resolutions, but not something in between. The proposed FGSS scheme provides a Fine-Granularity property to the spatial scalability. In this scheme, the bit plane technique is combined with spatial scalability, thus a Fine Granularity increase in the image quality from low-resolution to high-resolution can be obtained. In addition, the proposed scheme provides a flexible embedded bitstream that can be decoded up to any point in the enhancement layer bitstream from low-resolution to high-resolution. This feature further enables efficient video streaming over the Internet where the scalable bitstream, can adapt to the widely fluctuating bandwidth. The FGSS coding scheme extends new functionalities such as multi-resolution, Fine Granularity, channel adaptation and error-recovery properties to scalable video coding, thus it can satisfy different user clients with a wide range of channel bandwidth and screen resolution.
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ITCC - Optimal rate allocation for progressive Fine Granularity scalable video coding
Proceedings International Conference on Information Technology: Coding and Computing, 1Co-Authors: Qi Wang, Zixiang XiongAbstract:We examine the enhancement-layer rate allocation problem in progressive Fine Granularity scalable (PFGS) video coding. The problem arises from the fact that different frames in the enhancement layer have different rates in PFGS coding. A rate-distortion (R-D) function for a multi-frame group is first established for enhancement-layer PFGS coding, followed by experiments to verify its validity using real test sequences. Optimal rate allocation among frames in the group is then given based on the R-D function, together with a simple implementation that is suitable for applications like streaming video. Experiments show that, compared with uniform bit allocation, optimal bit allocation not only makes the quality variation in decoded video much smoother, but also improves the average PSNR of PFGS coding by 0.3-0.5 dB.