MPEG-4 FGS coding performance improvement using adaptive inter-layer prediction

MPEG-4 fine granularity coding (FGS) has been introduced as a standard video coding tool for video streaming over heterogeneous networks (e.g., the Internet and wireless networks) for its flexibility in supporting a large-range of bit-rates and good error robustness. However, the flexibility and error robustness of MPEG-4 FGS comes with the expense of significantly lower coding efficiency than single-layer coding. To improve the coding efficiency, we propose a three-mode codec architecture which introduces part of the enhancement-layer (EL) bitplanes into the motion-compensated prediction (MCP) loops of base-layer (BL) and EL. We propose a novel two-pass adaptive inter-layer prediction scheme which collects coding statistics of macroblocks (MB) in the first-pass encoding, and then, in the second pass, dynamically chooses the prediction mode by properly reaching a good tradeoff between the estimated coding gain and drifting error with the fine prediction. We also propose an adaptive bit-allocation method which truncates the FGS EL bitstream at the streaming server according to the channel bandwidth to further improve the coding efficiency. Experimental results show that the proposed method can achieve average PSNR improvement by 1.2∼1.4 dB over the baseline FGS, while keeping low drifting error at low channel rates.