Mode-adaptive fine granularity scalability
Wen-Shiaw Peng, Yen-Kuang Chen · 2002
We propose a new algorithm, which utilizes the enhancement layer prediction to further improve the coding efficiency of current fine granularity scalability (FGS) defined in MPEG-4. The proposed algorithm adaptively uses (1) the previously reconstructed enhancement layer macroblock after motion compensation along with (2) current reconstructed base layer macroblock and (3) the combination of both to form the predicted macroblock for the current enhancement layer. The new algorithm is designed so that other error drifting reduction methods can be used to avoid the drifting problem due to prediction from the enhancement layer. In addition, the proposed algorithm can re-use the implemented B-frame hardware to form the enhancement layer predicted frame. Simulation results show that about 1 dB gain in PSNR can be achieved compared to the current FGS algorithm at moderate to high bit rates. Thus, the proposed algorithm is a cost efficient solution to improve the coding efficiency of fine granularity scalability.