Two-Step Fast Mode Decision for Intra Coding of Screen Content

With the rapid development of screen content video applications, screen content coding (SCC) is urgently needed to be used in commercial codecs. However, the extra encoding complexity introduced by the new SCC tools has posed a great challenge for its practical deployment. In this paper, motivated by our observations that there should be a fine-grained mapping between image content and candidate modes, we propose a two-step fast mode decision method to reduce the encoding complexity. First, we propose to use a convolution neural network (CNN) to automatically extract useful features for fine-grained content classification. Second, we build a precise and concise mapping from CUs to candidate modes by simultaneously considering CU content type, CU size, and mode complexity. Note that the spatial correlations between neighboring CUs and current CU are also utilized in candidate modes derivation. In addition to the two-step fast mode decision method, a content-aware early termination algorithm is further proposed to reduce the encoding complexity. Extensive experiments demonstrate that our method achieves better performance compared with state-of-the-art ones, with 50.13% total encoding complexity reduction and only 0.92% BD-rate increase.

I. Introduction

Recently, more and more screen content video applications have emerged, including screen sharing, wireless displays, remote education, and so on, which have posed great challenges to the traditional video coding methods [1]. Video content in these applications is mainly computer-generated which is typically composed of some repeated patterns, massively flat areas, limited color values, and without sensor noises. These characteristics, however, are not exploited by the traditional coding methods in H.264/Advanced Video Coding [2] and H.265/High Efficiency Video coding (HEVC) [3]. Two other screen content coding (SCC) modes, known as Palette mode (PLT) [4] and Intra Block Copy (IBC) [5], have been proposed to improve the compression performance for screen content videos. Some further improvements can be found in [6]–[13] for these two coding tools.