Screen Content Coding (SCC) is an extension of High Efficiency Video Coding by adopting new coding modes to improve the coding efficiency of SCC at the expense of increased complexity. This paper proposes an online-learning approach for fast mode decision and coding unit (CU) size decision in SCC. To make fast mode decision, the corner point is firstly extracted as a unique feature in screen content, which is an essential preprocessing step to guide Bayesian decision modelling. Second, distinct color number in a CU is derived as another unique feature in screen content to build the precise model using online-learning for skipping unnecessary modes. Third, the correlation of the modes among spatial neighboring CUs is analyzed to further eliminate unnecessary mode candidates. Finally, the Bayesian decision rule using online-learning is applied again to make fast CU size decision. To ensure the accuracy of the Bayesian decision models, new scene change detection is designed to update the models. Results show that the proposed algorithm achieves 36.69% encoding time reduction with 1.08% Bjxntegaard delta bitrate (BDBR) increment under All Intra configuration. By integrating into the existing fast SCC approach, the proposed algorithm reduces 48.83% encoding time with 1.78% increase in BDBR.

The Screen Content Coding (SCC) extension of High Efficiency Video Coding (HEVC) improves coding gain for screen content videos by introducing two new coding modes, intra block copy (IBC) and palette (PLT) modes. However, the coding gain is achieved at the increased cost of computational complexity. In this paper, we propose a decision tree based framework for fast intra mode decision by investigating various features in training sets. To avoid the exhaustive mode searching process, a sequential arrangement of decision trees is proposed to check each mode separately by inserting a classifier before checking a mode. As compared with the previous approaches that both IBC and PLT modes are checked for screen content blocks (SCBs), the proposed coding framework is more flexible which facilitates either IBC or PLT mode to be checked for SCBs such that computational complexity is further reduced. To enhance the accuracy of decision trees, dynamic features are introduced which reveal the unique intermediate coding information of a coding unit (CU). Then, if all modes are decided to be skipped for a CU at the last depth level, at least one possible mode is assigned by a CU type decision tree. Furthermore, a decision tree constraint technique is developed to reduce the rate-distortion performance loss. Compared with the HEVC-SCC reference software SCM-8.3, the proposed algorithm reduces computational complexity by 47.62% on average with a negligible Bjøntegaard delta bitrate (BDBR) increase of 1.42% under all-intra (AI) configuration, which outperforms all state-ofthe- art algorithms in the literature.

A Screen Content Coding (SCC) extension to High Efficiency Video Coding (HEVC) has been developed to incorporate many new coding tools in order to achieve better coding efficiency for videos mixed with camera-captured content and graphics/text/animation. For instance, Intra Block Copy (IntraBC) mode helps to encode repeating patterns within the same frame while Palette mode aims at encoding screen content with few major colors. However, IntraBC mode brings along high computational complexity due to the exhaustive block matching within the same frame though there are already some constraints and fast approaches applied to IntraBC mode to reduce its complexity. Thus, we propose a fast intra coding scheme to reduce the complexity of using IntraBC mode in SCC. Screen content always contains no sensor noise resulting in the characteristics with pixel exactness along both horizontal and vertical directions. These characteristics pave the way for mode skipping and early Coding Unit (CU) splitting. Besides, early CU pruning and early termination are proposed based on the Rate Distortion (RD) cost to further reduce the encoder complexity. Moreover, we also propose to reduce the complexity of IntraBC mode by checking the hash value of each block candidate and the current block during block matching. With our proposed scheme, the encoding time is reduced compared with the SCC while the coding efficiency can still be maintained with minor increase in Bjontegaard Delta Bitrate (BDBR).

Multiview video plus depth format has been adopted as the emerging 3D video representation recently. It includes a limited number of textures and depth maps to synthesize additional virtual views. Since the quality of depth maps influences the view synthesis process, their sharp edges should be well preserved to avoid mixing foreground with background. To address this issue, 3D-High Efficiency Video Coding (HEVC) introduces new coding tools, a partition-based intra mode [depth modeling mode (DMM)], a residual description technique [segmentwise depth coding (SDC)], and a more complex rate-distortion (RD) evaluation with view synthesis optimization (VSO), to provide more accurate predictions and achieve higher compression rate. However, these new techniques introduce a lot of possible candidates, and each of them requires complicated RD calculation in the process of intra-mode decision. They lead to unacceptable computational burden in a 3D-HEVC encoder. Therefore, in this paper, we raise two efficient techniques for depth intra-mode decision. First, by investigating the statistical characteristics of variance distributions in the two partitions of DMM, a simple but efficient criterion based on the squared Euclidean distance of variances (SEDV) is suggested to evaluate RD costs of the DMM candidates instead of the time-consuming VSO process. Second, a probability-based early depth intra-mode decision is proposed to select only the most promising mode and make the early determination of using SDC based on the low-complexity RD cost in rough mode decision. Experimental results show that the proposed algorithm with these two new techniques provides 33%-48% time reduction with little drop in the coding performance compared with the state-of-the-art algorithms.

Screen content coding (SCC) is the extension to High Efficiency Video Coding (HEVC) for compressing screen content videos. New coding tools, intra block copy (IBC) and palette (PLT) modes, are introduced to encode screen content (SC) such as texts and graphics. IBC mode is used for encoding repeating patterns by performing block matching within the same frame, while PLT mode is designed for SC with few distinct colors by coding the major colors and their corresponding locations using an index map. However, the use of IBC and PLT modes increases the encoder complexity remarkably though coding efficiency can be improved. Therefore, we propose to have a mode skipping approach to reduce the encoder complexity of SCC by making use of SC characteristics, neighbor coding unit (CU) correlations, and intermediate cost information via random forest (RF). Detailed feature analyses and sample preparation are also described. A novel hyperparameter tuning approach with the consideration of coding bitrate and encoding time is proposed for RFs at each CU size to further boost the encoding process. Experimental results show that our proposed approach can obtain 45.06% average encoding time reduction with only 1.08% increase in Bjontegaard delta bitrate (BD-rate). Average encoding time can even be reduced to 58.57% by regulating the hyperparameters.

This paper presents fast and efficient intra prediction algorithms for screen content coding (SCC). The proposed algorithms focus on smooth regions frequently appeared in screen content videos, which have the characteristics of noiselessness. All the samples in a noiseless smooth region exhibit exactly the same pixel value. We then propose two intra coding techniques for noiseless smooth regions in SCC based on the smoothness of the boundary samples which are used for intra prediction. Our proposed algorithm can reduce computational complexity by at most 26.7% while keeping nearly the same video quality. Moreover, by removing the redundant coding bits for intra prediction modes, computational complexity can be further reduced to at most 53.3% in terms of encoding time with bitrate reduction up to 1.2%.