Efficient intra‐mode decision algorithm skipping unnecessary depth‐modelling modes in 3D‐HEVC

Park, Chun-Su

doi:10.1049/el.2014.3874

Cited by 14 publications

(14 citation statements)

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“…Shen et al in [29] and Min et al in [30] follow similar approaches that consider texture homogeneity and directional edge complexities, respectively, to determine the CU size. Texture features of the spatial CUs and co-located CU are used in [31] for fast CU size decisions in 3D-HEVC. Furthermore, texture edges identified using Sobel kernels are utilised to early predict the angular prediction direction in HEVC intra-prediction in [32].…”

Section: Texture Properties-based Methodsmentioning

confidence: 99%

Quality of Experience (QoE)-Aware Fast Coding Unit Size Selection for HEVC Intra-Prediction

et al. 2019

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The exorbitant increase in the computational complexity of modern video coding standards, such as High Efficiency Video Coding (HEVC), is a compelling challenge for resource-constrained consumer electronic devices. For instance, the brute force evaluation of all possible combinations of available coding modes and quadtree-based coding structure in HEVC to determine the optimum set of coding parameters for a given content demand a substantial amount of computational and energy resources. Thus, the resource requirements for real time operation of HEVC has become a contributing factor towards the Quality of Experience (QoE) of the end users of emerging multimedia and future internet applications. In this context, this paper proposes a content-adaptive Coding Unit (CU) size selection algorithm for HEVC intra-prediction. The proposed algorithm builds content-specific weighted Support Vector Machine (SVM) models in real time during the encoding process, to provide an early estimate of CU size for a given content, avoiding the brute force evaluation of all possible coding mode combinations in HEVC. The experimental results demonstrate an average encoding time reduction of 52.38%, with an average Bjøntegaard Delta Bit Rate (BDBR) increase of 1.19% compared to the HM16.1 reference encoder. Furthermore, the perceptual visual quality assessments conducted through Video Quality Metric (VQM) show minimal visual quality impact on the reconstructed videos of the proposed algorithm compared to state-of-the-art approaches.

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Section: Texture Properties-based Methodsmentioning

confidence: 99%

Quality of Experience (QoE)-Aware Fast Coding Unit Size Selection for HEVC Intra-Prediction

et al. 2019

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“…In addition to these four types of fast depth intra mode decision, Mora et al [40] exploited texture-depth redundancies and developed an inter-component coding tool, in which the depth quadtree is limited to the coded texture quadtree. In [41], a RD cost-based fast algorithm was designed to skip DMM calculation and SDC testing for DMM candidates. Based on statistical analysis of intra mode, Zhang et al [42] jointly reduced the number of the conventional intra mode candidates and simplified DMM decision.…”

Section: Related Workmentioning

confidence: 99%

Probability-Based Depth Intra-Mode Skipping Strategy and Novel VSO Metric for DMM Decision in 3D-HEVC

Zhang

Chan

et al. 2018

IEEE Trans. Circuits Syst. Video Technol.

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Multi-view video plus depth (MVD) format has been adopted as the emerging 3D video representation recently. It includes a limited number of textures and depth maps to synthesize additional virtual views. Since the quality of depth maps influences the view synthesis process, their sharp edges should be well preserved to avoid mixing foreground with background. To address this issue, 3D-HEVC introduces new coding tools, a partition-based intra mode (depth modelling mode, DMM), a residual description technique (segment-wise depth coding, SDC), and a more complex Rate-Distortion (RD) evaluation with view synthesis optimization (VSO), to provide more accurate predictions and achieve higher compression rate. However, these new techniques introduce a lot of possible candidates and each of them requires complicated RD calculation in the process of intra mode decision. They lead to unacceptable computational burden in a 3D-HEVC encoder. Therefore, in this paper, we raise two efficient techniques for depth intra mode decision. First, by investigating the statistical characteristics of variance distributions in the two partitions of DMM, a simple but efficient criterion based on the squared Euclidean distance of variances (SEDV) is suggested to evaluate RD costs of the DMM candidates instead of the time-consuming VSO process. Second, a probability-based early depth intra mode decision (PBED) is proposed to select only the most promising mode and make the early determination of using SDC based on the low complexity RD-Cost in rough mode decision. Experimental results show that the proposed algorithm with these two new techniques provides 33%-48% time reduction with little drops of the coding performance compared with the state-of-the-art algorithms.

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“…Therefore, the HEVC video coding framework cannot be perfectly applied to the depth map. To this end, 3D-HEVC employs many complicated coding algorithms for depth map coding, including depth modeling mode (DMM) [11], segment-wise DC coding (SDC) [12], motion parameter inheritance (MPI) [13], and view synthesis optimization (VSO) [14]. Although these coding algorithms improve the compression efficiency, they also introduce increasing greater computational complexity.…”

Section: Introductionmentioning

confidence: 99%

Fast Depth Intra Coding Based on Depth Edge Classification Network in 3D-HEVC

Liu

Jia

Liu

2022

IEEE Trans. on Broadcast.

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As the extension of high efficiency video coding (HEVC) standard, three dimensional-HEVC (3D-HEVC) is the latest 3D video coding standard. 3D-HEVC adopts many complicated coding algorithms to generate additional intermediate views for 3D video representation, which result in extremely high coding complexity. Therefore, this paper proposed a fast depth intra coding approach to reduce the 3D-HEVC complexity, which is based on convolutional neural network (CNN). First, we established a database based on the independent view of the depth map, which includes coding unit (CU) partition data of the depth map. Second, we constructed a depth edge classification CNN (DEC-CNN) framework to classify the edges for the depth map and embedded the network into a 3D-HEVC test platform. Finally, we utilized the pixel value of the binarized depth image to correct the above classification results. The experimental results demonstrated that our approach can reduce the intra coding time by 72.5% on average under negligible degradation of coding performance. This result outperforms the other state-of-the-art methods to reduce the coding complexity of 3D-HEVC.

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Efficient intra‐mode decision algorithm skipping unnecessary depth‐modelling modes in 3D‐HEVC

Cited by 14 publications

References 1 publication

Quality of Experience (QoE)-Aware Fast Coding Unit Size Selection for HEVC Intra-Prediction

Quality of Experience (QoE)-Aware Fast Coding Unit Size Selection for HEVC Intra-Prediction

Probability-Based Depth Intra-Mode Skipping Strategy and Novel VSO Metric for DMM Decision in 3D-HEVC

Fast Depth Intra Coding Based on Depth Edge Classification Network in 3D-HEVC

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