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• W1838672068 abstract "We propose an improved coarse-grain scalable video coding (SVC) approach based on the structural similarity (SSIM) index as the visual quality criterion, aiming at maximizing the overall coding performance constrained by user-defined quality weightings for all scalable layers. First, we develop an interlayer rate-SSIM dependency model, by investigating bit rate and SSIM relationships between different layers. Second, a reduced-reference SSIM-Q model and a Laplacian R-Q model are introduced for SVC, by incorporating the characteristics of hierarchical prediction structure in each layer. Third, based on the user-defined weightings and the proposed models, we design a rate-distortion optimization approach to adaptively adjust Lagrange multipliers for all layers to maximize the overall rate-SSIM performance of the scalable encoder. Experiments with multiple layers, different layer weightings, and various videos demonstrate that the proposed framework can achieve better rate-SSIM performance than single layer optimization method, and provide better coding efficiency as compared to the conventional SVC scheme. Subjective tests further demonstrate the benefits of the proposed scheme." @default.
• W1838672068 created "2016-06-24" @default.
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• W1838672068 date "2015-06-01" @default.
• W1838672068 modified "2023-09-24" @default.
• W1838672068 title "SSIM-Based Coarse-Grain Scalable Video Coding" @default.
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• W1838672068 doi "https://doi.org/10.1109/tbc.2015.2424012" @default.
• W1838672068 hasPublicationYear "2015" @default.
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