Rate distortion bounds for blocking and intra-frame prediction in videos

Recently we proposed a block-based conditional correlation coefficient model for natural videos in the spatial-temporal domain. The conditioning is on local texture and the optimal parameters can be calculated for a specific video with a mean absolute error (MAE) usually smaller than 5%. We used this conditional correlation model and the classic results on conditional rate distortion functions to calculate new theoretical rate distortion bounds for videos which appear to be the only valid theoretical rate distortion bounds with regard to the current cutting-edge video compression technologies such as those standardized in AVC/H.264. In this paper, we focus on utilizing the new block-based local-texture-dependent correlation model to derive rate distortion bounds for blocking and optimal prediction across neighboring blocks. We study the penalty paid in average rate when the correlation among the neighboring blocks is discarded completely or is incorporated partially through predictive coding. We calculate the thresholds in average rate and distortion when incorporating the correlation among the neighboring blocks through optimal predictive coding becomes worse than completely discarding this correlation. We also discuss the role of local texture in inter-frame prediction.