Fast Depth and Mode Decision in Intra Prediction for Quality SHVC

Abstract

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.

Probability-Based Fast Intra Prediction Algorithm for Spatial SHVC

“…However, these works do not consider probabilities to early terminate modes or depth selection, which limit the improvement of coding speed. In order to address this issue, the work in [18] predicts the probabilities of depths and modes based on correlations and correlation degrees, and then evenly divides the probabilities into three groups. In each group, a fixed threshold is used to early terminate ILR mode and depth selection.…”

“…The Interlayer and spatio-temporal correlation are adopted in [20] to build two feedforwards neural network-based learning models to predict depths and modes. In brief, one common feature about the above works [15][16][17][18][19][20] is that correlation degrees are fully utilized in prediction.…”

“…Clearly, the fact that different depths and modes usually have different probabilities should be considered to obtain more accurate thresholds in prediction. The work in [18] divides probabilities into several groups and set a fixed value for each group in prediction. However, probabilities in each group are also different, which also requires variable thresholds instead of fixed ones.…”

“…Therefore, only using pixels in calculating correlation degrees may not always obtain the optimal performance. The other way is to use colocated correlation degrees in BL for quality SHVC [15][16][17][18]. However, in spatial SHVC, these correlation degrees cannot be obtained directly based on co-located correlation degrees in BL, since the resolutions in BL and EL vary between two scalability ratios (1.5x and 2x).…”

“…Thus, they certainly cannot obtain the optimum performance. The work [18] divides probabilities on depth and ILR into three groups. Each group has its fixed threshold for depth and ILR mode early termination.…”