Rate Control for Predictive Transform Screen Content Video Coding Based on RANSAC

Abstract

In predictive transform video coding, optimal bit allocation and quantization parameter (QP) estimation are important to control the bit rate of blocks, frames and the whole sequence. Common solutions to this problem rely on trained models to approximate the rate-distortion (R-D) characteristics of the video content during coding. Moreover, these solutions are mainly targeted for natural content sequences, whose characteristics differ greatly from those of screen content (SC) sequences. In this article, we depart from such trained R-D models and propose a low-complexity RC method for SC sequences that leverages the availability of information about the R-D characteristics of previously coded blocks within a frame. Namely, our method first allocates bits at the frame- and block-levels based on their motion and texture characteristics. It then approximates the R-D and R-QP curves of each block by a set control points and random sample consensus (RANSAC). Finally, it computes the appropriate block-level QP values to attain a target bit rate with the minimum distortion possible. The proposed RC method is embedded into a standard High-Efficiency Video Coding (H.265/HEVC) encoder and evaluated on several SC sequences. Our results show that our method not only attains better R-D performance than that of H.265/HEVC and other methods designed for SC sequences but also attains a more constant and higher reconstruction quality on all frames.