FGS+: optimizing the joint SNR-temporal video quality in MPEG-4 fine grained scalable coding
To enable video transmission over heterogeneous wireless networks, a highly scalable compression and streaming framework that can adapt to large and rapid bandwidth variations in real-time is necessary. MPEG-4 fine grained scalability (FGS) provides fine-grained SNR and temporal scalabilities, but these scalabilities are implemented and performed independently, thereby neglecting the gains that can be made from making joint SNR-temporal decisions to maximize quality. A novel fine grained SNR-temporal scalability framework called FGS+ that provides a new level of performance by considering SNR and temporal scalability jointly is presented. This new solution uses the results of our subjective tests, which indicate the levels to which SNR-quality needs to be enhanced before motion-smoothness should be improved. The study also reveals that these SNR-temporal tradeoff points vary among videos, and depend on the characteristics of the video. Based on these observations, our solution uses reference frames, enhanced relative to FGS, for prediction, improving visual quality over MPEG-4 FGS by up to 1.5 dB.