Sparse Coding of Intra Prediction Residuals for Screen Content Coding

Abstract

High Efficiency Video Coding - Screen Content Coding (HEVC-SCC) is an extension to HEVC which adds sophisticated compression methods for computer generated content. A video frame is usually split into blocks that are predicted and subtracted from the original, which leaves a residual. These blocks are transformed by integer discrete sine transform (IntDST) or integer discrete cosine transform (IntDCT), quantized, and entropy coded into a bitstream. In contrast to camera captured content, screen content contains a lot of similar and repeated blocks. The HEVC-SCC tools utilize these similarities in various ways. After these tools are executed, the remaining signals are handled by IntDST/IntDCT which is designed to code camera-captured content. Fortunately, in sparse coding, the dictionary learning process which uses these residuals adapts much better and the outcome is significantly sparser than for camera captured content. This paper proposes a sparse coding scheme which takes advantage of the similar and repeated prediction residuals and targets low to mid frequency/energy blocks with a low sparsity setup. We also applied an approach which splits the common test conditions (CTC) sequences into categories for training and testing purposes. It is integrated as an alternate transform where the selection between traditional transform and our proposed method is based on a rate-distortion optimization (RDO) decision. It is integrated in HEVC-SCC test model (HM) HM- 16.18+SCM-8.7. Experimental results show that the proposed method achieves a Bjontegaard rate difference (BD-rate) of up to 4.6% in an extreme computationally demanding setup for the all intra configuration compared with HM-16.18+SCM-8.7.