Fast 3D-HEVC Depth Intra Coding Based on Boundary Continuity

H.266/Versatile Video Coding (VVC) is the latest international video coding standard to encode ultra-high-definition video effectively. The quadtree with nested multi-type tree (QT-MTT) structure provides various sizes of coding tree partitioning and allows the nested binary tree (BT) split and ternary tree (TT) split at each QT level. Furthermore, numerous advanced coding tools are equipped in the H.266/VVC encoder. However, the encoding time increases tremendously. Previous researches regarding the fast coding algorithm of H.266/VVC seldom mention perceptual redundancy. This paper utilizes the human vision model of just noticeable difference to extract the visually distinguishable pixels that may affect the visual perception. We observe that the distributions acquired by the horizontal and vertical projections of visually distinguishable pixels within the coding unit are related to their corresponding MTT splitting modes. Therefore, the distributions representing the perceptual information of human vision are used to be the input features of machine learning. Fast MTT decision determined by the random forest models of machine learning is proposed to quickly select the partition for intra coding. Experimental results demonstrate that the proposed method can effectively accelerate intra coding process while maintaining good bitrate and video quality based on the properties of the visual perception. The proposed algorithm provides better performance than the previous work.

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“…The performance of the proposed algorithm is compared with [22]. Time saving (TS) is calculated by (6) and coding gain (CG) is computed by (7).…”

Section: Resultsmentioning

confidence: 99%

Efficient Partition Decision Based on Visual Perception and Machine Learning for H.266/Versatile Video Coding

et al. 2022

Self Cite

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“…In the past several years, many approaches have been proposed to reduce the massive encoding complexity of various standards, such as HEVC and VVC. Works focusing on fast 3D-HEVC encoding have been developed [5][6][7][8]. Some researchers proposed fast video coding methods from the perspective of hardware design.…”

Section: Introductionmentioning

confidence: 99%

Bagged Tree and ResNet-Based Joint End-to-End Fast CTU Partition Decision Algorithm for Video Intra Coding

Peng

et al. 2022

Electronics

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Video coding standards, such as high-efficiency video coding (HEVC), versatile video coding (VVC), and AOMedia video 2 (AV2), achieve an optimal encoding performance by traversing all possible combinations of coding unit (CU) partition and selecting the combination with the minimum coding cost. It is still necessary to further reduce the encoding time of HEVC, because HEVC is one of the most widely used coding standards. In HEVC, the process of searching for the best performance is the source of most of the encoding complexity. To reduce the complexity of the coding block partition in HEVC, a new end-to-end fast algorithm is presented to aid the partition structure decisions of the coding tree unit (CTU) in intra coding. In the proposed method, the partition structure decision problem of a CTU is solved by a novel two-stage strategy. In the first stage, a bagged tree model is employed to predict the splitting of a CTU. In the second stage, the partition problem of a 32 × 32-sized CU is modeled as a 17-output classification task for the first time, so that it can be solved by a single prediction. To achieve a high prediction accuracy, a residual network (ResNet) with 34 layers is employed. Jointly using bagged tree and ResNet, the proposed fast CTU partition algorithm is able to generate the partition quad-tree structure of a CTU through an end-to-end prediction process, which abandons the traditional scheme of making multiple decisions at various depth levels. In addition, several datasets are used in this paper to lay the foundation for high prediction accuracy. Compared with the original HM16.7 encoder, the experimental results show that the proposed algorithm can reduce the encoding time by 60.29% on average, while the Bjøntegaard delta rate (BD-rate) loss is as low as 2.03%, which outperforms the results of most of the state-of-the-art approaches in the field of fast intra CU partition.

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Tunable early CU size decision for depth map intra coding in 3D-HEVC using unsupervised learning

Yang

et al. 2022

Digital Signal Processing

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Fast 3D-HEVC Depth Intra Coding Based on Boundary Continuity

Cited by 5 publications

References 32 publications

Efficient Partition Decision Based on Visual Perception and Machine Learning for H.266/Versatile Video Coding

Efficient Partition Decision Based on Visual Perception and Machine Learning for H.266/Versatile Video Coding

Bagged Tree and ResNet-Based Joint End-to-End Fast CTU Partition Decision Algorithm for Video Intra Coding

Tunable early CU size decision for depth map intra coding in 3D-HEVC using unsupervised learning

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