New Rate Distortion Bounds for Natural Videos Based on a Texture-Dependent Correlation Model

We revisit the classic problem of developing a spatial correlation model for natural images and videos by proposing a conditional correlation model for relatively nearby pixels that is dependent upon five parameters. The conditioning is on local texture and the optimal parameters can be calculated for a specific image or video with a mean absolute error usually smaller than 5%. We use this conditional correlation model to calculate the conditional rate distortion function when universal side information on local texture is available at both the encoder and the decoder. We demonstrate that this side information, when available, can save as much as 1 bit per pixel for selected videos at low distortions. We further study the scenario when the video frame is processed in macroblocks (MBs) or smaller blocks and calculate the rate distortion bound when the texture information is coded losslessly and optimal predictive coding is utilized to partially incorporate the correlation between the neighboring MBs or blocks. These rate distortion bounds are compared to the operational rate distortion functions generated in intra-frame coding using the H.264/AVC video coding standard.