Fast 3D-HEVC PU size decision algorithm for depth map intra-video coding

Hamout, Hamza; Elyousfi, Abderrahmane

doi:10.1007/s11554-019-00890-x

Cited by 7 publications

(9 citation statements)

References 36 publications

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“…However, computing the features that can describe the homogeneous depth map CTU can be used to predict the suitable sizes to be evaluated. In this paper, we use features different from those used in our previous contribution [16]. Hence, we select the amplitude of simplified mass center (ASMCV) and the variance [32] as the proposed homogeneous features.…”

Section: Clustering Algorithm and Feature Extractionmentioning

confidence: 99%

“…Recently, some research studies have been developed to accelerate the size decision process in 3D-HEVC intradepth map coding [10][11][12][13][14][15][16][17] and in HEVC/VVC [18,19]. In [10], the proposed method used a speci c feature called a corner point to accelerate the quadtree intradecision.…”

Section: Introductionmentioning

confidence: 99%

“…e authors in [15] exploit the statistical data analysis to create fast CU/PU size decisions to reduce the complexity of depth map intracoding. In our previously proposed algorithm [16], the depth map intrasize decision is established, including thresholds calculated based on an efficient clustering algorithm and tensor features. In [17], a low complexity intramode selection algorithm is proposed to reduce complexity of depth intraprediction in both intraframes and interframes.…”

Section: Introductionmentioning

confidence: 99%

“…is paper proposed an efficient size intramodel decision as an amelioration of our previous work [16] using an efficient clustering algorithm, namely, automatic merging possibilistic clustering method [20] and new features. e proposed method decreases the synthesized views efficiency losses while conserving the same coding time savings.…”

Section: Introductionmentioning

confidence: 99%

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A Computation Complexity Reduction of the Size Decision Algorithm in 3D-HEVC Depth Map Intracoding

Hamout

Elyousfi

2022

Advances in Multimedia

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A new video format called a multiview video plus depth map is recently designed as the most efficient 3D video representation. 3D-high efficiency video coding (3D-HEVC) is the international standard for 3D video coding finalized in 2016. In 3D-HEVC intracoding, the depth map is an essential component, in which the depth map intraprediction occupies more than 85% of the overall intraencoding time. In 3D-HEVC design, a highly flexible quadtree coding unit partitioning is adopted, in which one coding tree unit is partitioned recursively into a prediction unit (PU) from 64 × 64 down to 4 × 4. This highly flexible partitioning provides more accurate prediction signals and thereby achieves better intradepth map compression efficiency. However, performing all depth map intraprediction modes for each PU level achieves high depth map intracoding efficiency, but it results in a substantial computational complexity increase. This paper proposes an amelioration of the previously proposed depth map PU size decision using an efficient homogeneity determination. The resulting experiences show that the proposed method can significantly save the computational complexity with a negligible loss of intracoding efficiency.

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Section: Clustering Algorithm and Feature Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Computation Complexity Reduction of the Size Decision Algorithm in 3D-HEVC Depth Map Intracoding

Hamout

Elyousfi

2022

Advances in Multimedia

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“…Their approach with two 8 bit H.264/AVC codecs achieve similar results as 16 bit HEVC codec. In [32], [33], Hamout and Elyousfi improved the 3D-HEVC depth map intra prediction model for practical 3D applications. Their approach classified depth video regions as a homogenous or non-homogenous region.…”

Section: Related Workmentioning

confidence: 99%

A Flexible Lossy Depth Video Coding Scheme Based on Low-rank Tensor Modelling and HEVC Intra Prediction for Free Viewpoint Video

Sharma¹,

Kumar²

2021

Preprint

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The compression quality losses of depth sequences determine quality of view synthesis in free-viewpoint video. The depth map intra prediction in 3D extensions of the HEVC applies intra modes with auxiliary depth modeling modes (DMMs) to better preserve depth edges and handle motion discontinuities. Although such modes enable high efficiency compression, but at the cost of very high encoding complexity. Skipping conventional intra coding modes and DMMs in depth coding limits practical applicability of the HEVC for 3D display applications. In this paper, we introduce a novel low-complexity scheme for depth video compression based on low-rank tensor decomposition and HEVC intra coding. The proposed scheme leverages spatial and temporal redundancy by compactly representing the depth sequence as a high-order tensor. Tensor factorization into a set of factor matrices following CANDECOMP/PARAFAC (CP) decomposition via alternating least squares give a low-rank approximation of the scene geometry. Further, compression of factor matrices with HEVC intra prediction support arbitrary target accuracy by flexible adjustment of bitrate, varying tensor decomposition ranks and quantization parameters. The results demonstrate proposed approach achieves significant rate gains by efficiently compressing depth planes in low-rank approximated representation. The proposed algorithm is applied to encode depth maps of benchmark "Ballet" and "Breakdancing" sequences. The decoded depth sequences are used for view synthesis in a multi-view video system, maintaining appropriate rendering quality.

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