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

Wang, Dayong; Sun, Yu; Zhu, Ce; Li, Weisheng; Dufaux, Frédéric

doi:10.1109/tmm.2019.2937240

Cited by 18 publications

(14 citation statements)

References 34 publications

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“…Using correlations only, without considering correlation degrees, can certainly influence the performance. To improve the coding speed, Wang et al [15,16,17] exploit both correlations and correlation degrees to predict candidate depths. Then, they use residual coefficients and RD costs to early terminate mode and depth selection.…”

Section: Related Workmentioning

confidence: 99%

“…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.…”

Section: Related Workmentioning

confidence: 99%

“…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).…”

Section: Related Workmentioning

confidence: 99%

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Probability-Based Fast Intra Prediction Algorithm for Spatial SHVC

Wang

Sun

Liu

et al. 2022

IEEE Trans. on Broadcast.

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Due to multi-layer encoding and Inter-layer prediction, Scalable High Efficiency Video Coding (SHVC) has extremely high coding complexity. It is very crucial to improve its coding speed so as to promote widespread and cost-effective SHVC applications. In this paper, we propose a new probabilitybased fast Intra prediction algorithm for spatial SHVC. More specifically, first, we integrate depth probabilities with textural based all-zero blocks and all-nonzero blocks through Lagrange Interpolation Polynomial (LIP) to derive thresholds to early skip unlikely depths and early terminate depth selection. Second, to early skip Intra mode prediction, we combine mode probabilities with Jarque-Bera test through LIP to test the residual coefficients of Inter-layer reference (ILR) mode. Third, we adopt the difference of Hadamard Costs (HCs) of horizontal, vertical and diagonal directional modes to predict candidate directional modes (DMs), and then exploit both the percentages of gradient amplitudes and HCs for DM early termination. Experimental results demonstrate that the proposed algorithm can achieve a speed up gain of more than 61% with 0.03% decrease in Bjøntegaard Delta Bit Rate on average.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Probability-Based Fast Intra Prediction Algorithm for Spatial SHVC

Wang

Sun

Liu

et al. 2022

IEEE Trans. on Broadcast.

Self Cite

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show abstract

“…The current and previous frames have also high temporal correlation. Therefore, we can use spatial and temporal correlations to predict probable depths and skip unlikely ones, hence improving the coding speed [40][41]. The spatial and temporal correlations are exploited through the neighboring CUs of the current CU, as shown in Fig.2.…”

Section: A Correlation-based Depth Prediction (Cbdp)mentioning

confidence: 99%

Fast Depth and Mode Decision in Intra Prediction for Quality SHVC

Wang

Sun

Zhu

et al. 2020

IEEE Trans. on Image Process.

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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.

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“…Kuo et al [7] proposed an efficient and fast CU size decision algorithm to reduce HEVC encoder complexity by the spatiotemporal features. In [21], Wang et al proposed a new depth level and inter-mode prediction algorithm for quality scalable high efficiency video coding (SHVC). They investigated the relationship between parent CUs and children CUs to predict square modes, and used RD cost and residual coefficients in further predicting non-square modes to improve the coding speed.…”

Section: Related Workmentioning

confidence: 99%

Bagged Tree Based Frame-Wise Beforehand Prediction Approach for HEVC Intra-Coding Unit Partitioning

Peng

et al. 2020

Electronics

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High Efficiency Video Coding (HEVC) has achieved about 50% bit-rates saving compared with its predecessor H.264 standard, while the encoding complexity increases dramatically. Due to the introduction of more flexible partition structures and more optional prediction directions, HEVC takes a brute force approach to find the optimal partitioning result which is much more time consuming. Therefore, this paper proposes a bagged trees based fast approach (BTFA) and focuses on the coding unit (CU) size decision for HEVC intra-coding. First, several key features of a target CU are extracted for three-output classifiers. Then, to avoid feature extraction and prediction time over head, our approach is designed frame-wisely, and the procedure is applied parallel with the encoding process. Using the adaptive threshold determination algorithm, our approach achieves 42.04% time saving with negligible 0.92% Bit-Distortion (BD)-rate loss. Furthermore, in order to calculate the optimal thresholds to balance BD-rate loss and complexity reduction, the neural network based mathematical fitting is added to BTFA, which is called the advanced bagged trees based fast approach (ABTFA). Finally, experimental results show that ABTFA achieves 47.87% time saving with only 0.96% BD-rate loss, which outperforms other state-of-the-art approaches.

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

Abstract: on average, the proposed algorithm can achieve a time saving of 71.14%, with a bit rate increase of 1.27%.

Cited by 18 publications

References 34 publications

Probability-Based Fast Intra Prediction Algorithm for Spatial SHVC

Probability-Based Fast Intra Prediction Algorithm for Spatial SHVC

Fast Depth and Mode Decision in Intra Prediction for Quality SHVC

Bagged Tree Based Frame-Wise Beforehand Prediction Approach for HEVC Intra-Coding Unit Partitioning

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