Smooth extraction of SVC fine-granular SNR scalable videos with a virtual-GOP-based rate-distortion modeling

Jun Sun; Wen Gao; Debin Zhao

doi:10.1117/12.765160

28 January 2008 Smooth extraction of SVC fine-granular SNR scalable videos with a virtual-GOP-based rate-distortion modeling

Jun Sun, Wen Gao, Debin Zhao

Author Affiliations +

Proceedings Volume 6822, Visual Communications and Image Processing 2008; 68220N (2008) https://doi.org/10.1117/12.765160
Event: Electronic Imaging, 2008, San Jose, California, United States

Abstract

Fine-Granular SNR scalable (FGS) technologies in H.264/AVC-based scalable video coding (SVC) provide a flexible and effective foundation for scaling FGS enhancement layer (EL) to accommodate different and variable network capacities. To support smooth quality extraction of SVC FGS videos, it's important to obtain the Rate-Distortion (R-D) function of each picture or group of pictures (GOP). In this paper, firstly, we introduce the R-D analysis of SVC FGS coding in our prior work. Then, with the analysis and models, we present virtual GOP concept and a virtual-GOP-based packet scheduling algorithm is proposed to acquire the optimal packet scheduling sequence in a virtual GOP. Based on the packet scheduling algorithm and the R-D analysis of FGS EL, an effective and flexible D-R model is proposed to describe the D-R function of the virtual GOP. Then, with the R-D model of virtual GOPs, a practical non-search algorithm for smooth quality reconstruction is introduced. Compared to the quality layer method, the reconstructed video quality is improved not only objectively but also subjectively.

Citation Download Citation

Jun Sun, Wen Gao, and Debin Zhao "Smooth extraction of SVC fine-granular SNR scalable videos with a virtual-GOP-based rate-distortion modeling", Proc. SPIE 6822, Visual Communications and Image Processing 2008, 68220N (28 January 2008); https://doi.org/10.1117/12.765160

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