HEVC-based 3D holoscopic video coding using self-similarity compensated prediction

Conti, Caroline; Soares, Luís Ducla; Nunes, Paulo

doi:10.1016/j.image.2016.01.008

Cited by 84 publications

(109 citation statements)

References 30 publications

(56 reference statements)

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“…Since each pixel in a MAI represents a separate viewing angle, therefore the correlation within each MAI actually represents angular correlation. Recent LF compression methods proposed by Li et al [11] and Conti et al [12], [13] both work on MLIs. Li et al [11] attempted to remove the angular correlation via disparity guided view interpolation based on a sparse set of sub-sampled angular examples.…”

Section: A Motivationmentioning

confidence: 99%

“…Li et al [11] attempted to remove the angular correlation via disparity guided view interpolation based on a sparse set of sub-sampled angular examples. Conti et al [12], [13] proposed the self-similarity concept to compensate repeated MAI patterns. Both methods can efficiently remove the angular correlation in the LF.…”

Section: A Motivationmentioning

confidence: 99%

“…The l 0 -norm ||·|| 0 in Eqn. (12) calculates the number of non-zero elements in the vector α i,Θ(P i ) .…”

Section: E Disparity Guided Light Field Sparse Codingmentioning

confidence: 99%

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Light Field Compression With Disparity-Guided Sparse Coding Based on Structural Key Views

Chen

Hou

Chau

2018

IEEE Trans. on Image Process.

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Recent imaging technologies are rapidly evolving for sampling richer and more immersive representations of the 3D world. One of the emerging technologies is light field (LF) cameras based on micro-lens arrays. To record the directional information of the light rays, a much larger storage space and transmission bandwidth are required by an LF image as compared with a conventional 2D image of similar spatial dimension. Hence, the compression of LF data becomes a vital part of its application. In this paper, we propose an LF codec with disparity guided Sparse Coding over a learned perspective-shifted LF dictionary based on selected Structural Key Views (SC-SKV). The sparse coding is based on a limited number of optimally selected SKVs; yet the entire LF can be recovered from the coding coefficients. By keeping the approximation identical between encoder and decoder, only the residuals of the non-key views, disparity map, and the SKVs need to be compressed into the bit stream. An optimized SKV selection method is proposed such that most LF spatial information can be preserved. To achieve optimum dictionary efficiency, the LF is divided into several coding regions, over which the reconstruction works individually. Experiments and comparisons have been carried out over benchmark LF data set, which show that the proposed SC-SKV codec produces convincing compression results in terms of both rate-distortion performance and visual quality compared with Joint Exploration Model: with 37.9% BD-rate reduction and 1.17-dB BD-PSNR improvement achieved on average, especially with up to 6-dB improvement for low bit rate scenarios.

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Section: A Motivationmentioning

confidence: 99%

Section: A Motivationmentioning

confidence: 99%

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Light Field Compression With Disparity-Guided Sparse Coding Based on Structural Key Views

Chen

Hou

Chau

2018

IEEE Trans. on Image Process.

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“…The micro-image is the set of pixels behind each micro-lens, and is also sometimes called elemental image. Spatial prediction modes have thus been proposed for unfocused cameras in [15] and [42] based on a concept of self-similarity compensated prediction or using locally linear embedding techniques in [43]. These selfsimilarity prediction modes have been further extended to bidirectional prediction in [18] and [19].…”

Section: B Light Fields Compressionmentioning

confidence: 99%

Light Field Compression With Homography-Based Low-Rank Approximation

Jiang

Pendu

Farrugia

et al. 2017

IEEE J. Sel. Top. Signal Process.

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Abstract-This paper describes a light field compression scheme based on a novel homography-based low rank approximation method called HLRA. The HLRA method jointly searches for the set of homographies best aligning the light field views and for the low rank approximation matrices. The light field views are aligned using either one global homography or multiple homographies depending on how much the disparity across views varies from one depth plane to the other. The light field low-rank representation is then compressed using HEVC. The best pair of rank and QP parameters of the coding scheme, for a given target bit-rate, is predicted with a model defined as a function of light field disparity and texture features. The results are compared with those obtained by directly applying HEVC on the light field views re-structured as a pseudo-video sequence. The experiments using different data sets show substantial PSNR-rate gain of our compression algorithm, as well as the accuracy of the proposed parameter prediction model, especially for real light fields. A scalable extension of the coding scheme is finally proposed.

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“…As we know, in the 1-D hierarchical coding structure, the depth first coding order can lead to the least reference buffer size [14]. For example, The encoding order of a typical 1-D hierarchical coding structure with GOP size 16 is 0, 16,8,4,2,1,3,6,5,7,12,10,9,11,14,13,15. And the optimal size of the reference buffer is 5.…”

Section: Introductionmentioning

confidence: 99%

Pseudo-Sequence-Based 2-D Hierarchical Coding Structure for Light-Field Image Compression

et al. 2017

IEEE J. Sel. Top. Signal Process.

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In this paper, we present a novel pseudo sequence based 2-D hierarchical reference structure for light-field image compression. In the proposed scheme, we first decompose the lightfield image into multiple views and organize them into a 2-D coding structure according to the spatial coordinates of the corresponding microlens. Then we mainly develop three technologies to optimize the 2-D coding structure. First, we divide all the views into four quadrants, and all the views are encoded one quadrant after another to reduce the reference buffer size as much as possible. Inside each quadrant, all the views are encoded hierarchically to fully exploit the correlations between different views. Second, we propose to use the distance between the current view and its reference views as the criteria for selecting better reference frames for each inter view. Third, we propose to use the spatial relative positions between different views to achieve more accurate motion vector scaling. The whole scheme is implemented in the reference software of High Efficiency Video Coding. The experimental results demonstrate that the proposed novel pseudo-sequence based 2-D hierarchical structure can achieve maximum 14.2% bit-rate savings compared with the state-of-the-art light-field image compression method.

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HEVC-based 3D holoscopic video coding using self-similarity compensated prediction

Cited by 84 publications

References 30 publications

Light Field Compression With Disparity-Guided Sparse Coding Based on Structural Key Views

Light Field Compression With Disparity-Guided Sparse Coding Based on Structural Key Views

Light Field Compression With Homography-Based Low-Rank Approximation

Pseudo-Sequence-Based 2-D Hierarchical Coding Structure for Light-Field Image Compression

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