The Experts below are selected from a list of 39906 Experts worldwide ranked by ideXlab platform
Yan Feng - One of the best experts on this subject based on the ideXlab platform.
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perceptual based sao rate distortion optimization method with a simplified jnd model for h 265 hevc
Signal Processing-image Communication, 2015Co-Authors: Kaifang Yang, Shuai Wan, Yanchao Gong, Yan FengAbstract:In the latest H.265/High Efficiency Video Coding (HEVC) standard, the sample adaptive offset (SAO) filter technique is adopted to improve the quality of the reconstructed video. But so far, the research efforts related to the SAO optimization to date have mainly centered on objective rate-distortion (R-D) performance without considering its Visual quality. In this paper, the human Visual Characteristics (represented by a JND model) are introduced into the SAO optimization process for the first time, and a new human Visual perceptual-based SAO R-D optimization method, referred to as P-SAO, for H.265/HEVC is proposed. Simultaneously, considering the SAO R-D optimization in H.265/HEVC is complex and in order to use the JND model more effectively and to minimize the calculation time of the introduced JND in SAO, a simplified JND model is proposed based on a modified Sobel operator. Experimental results show that compared with the latest JND model in pixel domain, the proposed JND model can achieve similar subjective quality with significantly reduced computational complexity (i.e., an average processing time reduction of 89.35%). Compared with the original SAO R-D method in the reference software model of the H.265/HEVC, the P-SAO method can achieve better image subjective quality with performance gain of up to 0.2505dB in terms of Δ P S P N R without comprising the R-D performance in Δ P S N R . The human Visual Characteristic is first introduced into SAO optimization process.A new simplified just noticeable distortion (JND) model is proposed.A modified Sobel operator is proposed.The proposed method can achieve better image subjective quality.
Kaifang Yang - One of the best experts on this subject based on the ideXlab platform.
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perceptual based sao rate distortion optimization method with a simplified jnd model for h 265 hevc
Signal Processing-image Communication, 2015Co-Authors: Kaifang Yang, Shuai Wan, Yanchao Gong, Yan FengAbstract:In the latest H.265/High Efficiency Video Coding (HEVC) standard, the sample adaptive offset (SAO) filter technique is adopted to improve the quality of the reconstructed video. But so far, the research efforts related to the SAO optimization to date have mainly centered on objective rate-distortion (R-D) performance without considering its Visual quality. In this paper, the human Visual Characteristics (represented by a JND model) are introduced into the SAO optimization process for the first time, and a new human Visual perceptual-based SAO R-D optimization method, referred to as P-SAO, for H.265/HEVC is proposed. Simultaneously, considering the SAO R-D optimization in H.265/HEVC is complex and in order to use the JND model more effectively and to minimize the calculation time of the introduced JND in SAO, a simplified JND model is proposed based on a modified Sobel operator. Experimental results show that compared with the latest JND model in pixel domain, the proposed JND model can achieve similar subjective quality with significantly reduced computational complexity (i.e., an average processing time reduction of 89.35%). Compared with the original SAO R-D method in the reference software model of the H.265/HEVC, the P-SAO method can achieve better image subjective quality with performance gain of up to 0.2505dB in terms of Δ P S P N R without comprising the R-D performance in Δ P S N R . The human Visual Characteristic is first introduced into SAO optimization process.A new simplified just noticeable distortion (JND) model is proposed.A modified Sobel operator is proposed.The proposed method can achieve better image subjective quality.
Shuai Wan - One of the best experts on this subject based on the ideXlab platform.
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perceptual based sao rate distortion optimization method with a simplified jnd model for h 265 hevc
Signal Processing-image Communication, 2015Co-Authors: Kaifang Yang, Shuai Wan, Yanchao Gong, Yan FengAbstract:In the latest H.265/High Efficiency Video Coding (HEVC) standard, the sample adaptive offset (SAO) filter technique is adopted to improve the quality of the reconstructed video. But so far, the research efforts related to the SAO optimization to date have mainly centered on objective rate-distortion (R-D) performance without considering its Visual quality. In this paper, the human Visual Characteristics (represented by a JND model) are introduced into the SAO optimization process for the first time, and a new human Visual perceptual-based SAO R-D optimization method, referred to as P-SAO, for H.265/HEVC is proposed. Simultaneously, considering the SAO R-D optimization in H.265/HEVC is complex and in order to use the JND model more effectively and to minimize the calculation time of the introduced JND in SAO, a simplified JND model is proposed based on a modified Sobel operator. Experimental results show that compared with the latest JND model in pixel domain, the proposed JND model can achieve similar subjective quality with significantly reduced computational complexity (i.e., an average processing time reduction of 89.35%). Compared with the original SAO R-D method in the reference software model of the H.265/HEVC, the P-SAO method can achieve better image subjective quality with performance gain of up to 0.2505dB in terms of Δ P S P N R without comprising the R-D performance in Δ P S N R . The human Visual Characteristic is first introduced into SAO optimization process.A new simplified just noticeable distortion (JND) model is proposed.A modified Sobel operator is proposed.The proposed method can achieve better image subjective quality.
Yanchao Gong - One of the best experts on this subject based on the ideXlab platform.
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perceptual based sao rate distortion optimization method with a simplified jnd model for h 265 hevc
Signal Processing-image Communication, 2015Co-Authors: Kaifang Yang, Shuai Wan, Yanchao Gong, Yan FengAbstract:In the latest H.265/High Efficiency Video Coding (HEVC) standard, the sample adaptive offset (SAO) filter technique is adopted to improve the quality of the reconstructed video. But so far, the research efforts related to the SAO optimization to date have mainly centered on objective rate-distortion (R-D) performance without considering its Visual quality. In this paper, the human Visual Characteristics (represented by a JND model) are introduced into the SAO optimization process for the first time, and a new human Visual perceptual-based SAO R-D optimization method, referred to as P-SAO, for H.265/HEVC is proposed. Simultaneously, considering the SAO R-D optimization in H.265/HEVC is complex and in order to use the JND model more effectively and to minimize the calculation time of the introduced JND in SAO, a simplified JND model is proposed based on a modified Sobel operator. Experimental results show that compared with the latest JND model in pixel domain, the proposed JND model can achieve similar subjective quality with significantly reduced computational complexity (i.e., an average processing time reduction of 89.35%). Compared with the original SAO R-D method in the reference software model of the H.265/HEVC, the P-SAO method can achieve better image subjective quality with performance gain of up to 0.2505dB in terms of Δ P S P N R without comprising the R-D performance in Δ P S N R . The human Visual Characteristic is first introduced into SAO optimization process.A new simplified just noticeable distortion (JND) model is proposed.A modified Sobel operator is proposed.The proposed method can achieve better image subjective quality.
Lei Yingjie - One of the best experts on this subject based on the ideXlab platform.
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technique for image fusion based on nsst domain and human Visual Characteristics
Journal of Harbin Engineering University, 2013Co-Authors: Lei YingjieAbstract:A novel technique for image fusion based on the non-subsampled shearlet transform(NSST) domain and human Visual Characteristic(HVC) is proposed to resolve the problem of the multi-sensor image fusion.Multi-scale and multi-directional sparse decompositions of source images are performed by NSST,so that the low-frequency sub-images and a series of high-frequency ones with diverse scales and directions can be obtained.Then,as the evaluation norm of sub-images fusion,the definition of Visual sensitivity coefficient is presented to complete the fusion process of sub-images from each corresponding source image,respectively.Meanwhile,the algorithm for image fusion based on NSST and HVC is devised.The final fused image is achieved by utilizing inverse NSST to all fused sub-images.Experimental results show that the technique proposed has better performance,and higher running efficiency.
