Error drifting reduction in enhanced fine granularity scalability

Abstract

We incorporate fading and reset mechanisms in an enhanced fine granularity scalability algorithm to reduce the drifting error at low bit rate while still maintaining 1.5dB PSNR gain at high bit rate over the current MPEG-4 fine granularity scalability. Many previous works use enhancement layers to predict enhancement layers so as to increase the compression efficiency. Drifting error occurs because the enhancement layer, the predictor, is not received as expected. Our fading mechanism linearly combines the current reconstructed base layer and previously reconstructed enhancement layer with fading factors between 0 and 1. Our reset mechanism sets the reference frame for prediction to be the base layer periodically. Our theoretical formulation and experimental results show that drifting error can be distributed more uniformly and maximum accumulated mismatch error is significantly reduced while our mechanisms are turned on. Around 1dB can be improved at low bit rate comparing to the one without any drifting reduction mechanism.