A fast mode decision algorithm applied to Coarse-Grain quality Scalable Video Coding

IEEE Trans. on Image Process.

et al. 2020

Self Cite

Scalable High Efficiency Video Coding (SHVC) is the extension of High Efficiency Video Coding (HEVC). In intra prediction for quality SHVC, a Coding Unit (CU) is recursively divided into a quadtree-based structure from the largest 64×64 CU to the smallest 8×8 CU, in which 35 intra prediction modes and Inter-Layer Reference (ILR) mode are checked to determine the best possible mode. This leads to very high coding efficiency but also results in an extremely high coding complexity. To improve coding speed while maintaining coding efficiency, in this paper, we propose a new efficient algorithm for fast intra prediction for enhancement layer in SHVC. First, temporal and spatial correlations, as well as their correlation degrees, are combined in a Naive Bayes classifier to predict depth probabilities and skip depths with low likelihood. Second, for a given depth candidate, we combine ILR mode probability with Partial Zero Blocks (PZBs) based on the Sum of Squared Differences (SSD) to determine whether the ILR mode is the best one. In that case, we can skip intra prediction, which requires very high complexity. Third, initial Intra Modes (IMs) are obtained through Sobel operator, and are combined with the relationship between IMs and their corresponding Hadamard Cost (HC) values to predict candidate IMs in Rough Mode Decision (RMD). Then, an analytical criterion of early termination is developed based on the HC values of two neighboring IMs in the Rate-Distortion Optimization (RDO) process. Finally, we combine depth probabilities and the distribution of residual coefficients at the current depth to early terminate depth selection. The proposed scheme can significantly decrease the complexity of depth determination while reducing the complexity of mode decision for a depth candidate. Our experimental results demonstrate that the proposed scheme can achieve a speed up gain of more than 80% in average, while maintaining coding efficiency.

Section: Related Workmentioning

confidence: 99%

Fast Depth and Mode Decision in Intra Prediction for Quality SHVC

IEEE Trans. on Image Process.

et al. 2020

Self Cite

“…Based on motion activities, picture detail levels, inter-layer and spatial correlations, Lu et al [12] predict candidate modes and early terminate the encoding process. According to interlayer and spatial correlations, the work in [13] estimates likely modes, skips unlikely modes, and then early terminates the coding process based on the RD cost and residual coefficients. Generally speaking, these algorithms use correlations to either predict candidate modes or use early termination, or jointly exploit mode prediction and early termination to improve the coding speed.…”

Section: Related Workmentioning

confidence: 99%

Fast Depth and Inter Mode Prediction for Quality Scalable High Efficiency Video Coding

et al. 2020

IEEE Trans. Multimedia

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“…Lu et al [15] exploit inter-layer and neighboring correlations, as well as examine the level of picture details and motion activities to predict candidate modes and early terminate the encoding process. Wang et al [16] use inter-layer and spatial correlations to estimate candidate modes and exclude low likelihood modes, and make use of RD cost and residual coefficients to end the coding process early. In this way, the coding speed is effectively improved.…”

Section: Related Workmentioning

confidence: 99%

Efficient Multi-Strategy Intra Prediction for Quality Scalable High Efficiency Video Coding

IEEE Trans. on Image Process.

et al. 2019

Self Cite

As an extension of High Efficiency Video Coding (HEVC), the Scalable High Efficiency Video Coding (SHVC) introduces multiple layers with inter-layer predictions, which greatly increases the complexity on top of the already complicated HEVC encoder. In Intra prediction for Quality SHVC, Coding Tree Unit (CTU) allows recursive splitting into four depth levels, which considers 35 Intra prediction modes and interlayer reference (ILR) mode to determine the best possible mode at each depth level. This achieves the highest coding efficiency but incurs a substantially high computational complexity. In this paper, we propose a novel Intra prediction scheme to effectively speed up the enhancement layer Intra-coding in Quality SHVC. The new features of the proposed framework include: First, spatial correlation and its correlation degree are combined to predict most probable depth level candidates. Second, for a given depth candidate, based on the probabilities of the ILR mode, we check the ILR mode by examining the residual distribution based on skewness and kurtosis to determine whether the residuals follow a Gaussian distribution. In that case, the Intra prediction comparisons, which require a high complexity, are skipped. Third, during Intra prediction selection from 35 Intra prediction modes, spatial and inter-layer correlations are combined with the local monotonicity of the Hadamard costs associated with the modes in a small neighborhood, to examine only a portion of Intra prediction modes. Finally, a hypothesis testing on the currently selected depth level is performed to examine whether the residuals present significant differences within their block to early terminate depth selection. The proposed multi-step multistrategy scheme aims to minimize the number of depth selections while greatly reducing the mode decision complexity for a depth candidate in a hierarchical fashion. Our experimental results demonstrate that the proposed scheme can achieve a speedup gain of more than 75% in average on the test video sequences, while maintaining almost the same coding efficiency. .