HEVC-based light field image coding with bi-predicted self-similarity compensation

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

doi:10.1109/icmew.2016.7574667

Cited by 66 publications

(60 citation statements)

References 8 publications

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“…In [4] authors suggested to use HEVC Intra Profile to code the lenslet structure, and to improve its performance by integrating self-similarity compensated prediction and estimation. The proposed solution exploits the correlation between neighbooring micro-images in the lenslet image.…”

Section: Introductionmentioning

confidence: 99%

Objective and subjective evaluation of light field image compression algorithms

Viola

Řeřábek

Bruylants

et al. 2016

2016 Picture Coding Symposium (PCS)

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Abstract-This paper reports results of subjective and objective quality assessments of responses to a grand challenge on light field image compression. The goal of the challenge was to collect and evaluate new compression algorithms for light field images. In total seven proposals were received, out of which five were accepted for further evaluations. For objective evaluations, conventional metrics were used, whereas the double stimulus continuous quality scale method was selected to perform subjective assessments. Results show competitive performance among submitted proposals. However, in low bitrates, one proposal outperforms the others.

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Section: Introductionmentioning

confidence: 99%

Objective and subjective evaluation of light field image compression algorithms

Viola

Řeřábek

Bruylants

et al. 2016

2016 Picture Coding Symposium (PCS)

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“…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]. The authors in [16] introduce a bi-directional spatial prediction mode in HEVC for encoding elemental images captured by a focused 2.0 camera [44] which has been further extended in [20]) for unfocused 1.0 cameras.…”

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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“…The proposed techniques mostly focus on the compression multi-views and multi-focus from sub-aperture images derived from LF data [7]. A recent improvement is a self-similarity compensated prediction based HEVC coding solution for LF [8]. The key idea is to use bi-prediction of the blocks in a search window with similar information from the LF image.…”

Section: Introductionmentioning

confidence: 99%

Joint exploration model based light field image coding: A comparative study

Cong

Perry

et al. 2017

2017 4th NAFOSTED Conference on Information and Computer Science

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-The recent light field imaging technology has been attracting a lot of interests due to its potential applications in a large number of areas including Virtual Reality, Augmented Reality (VR/AR), Teleconferencing, and E-learning. Light Field (LF) data is able to provide rich visual information such as scene rendering with changes in depth of field, viewpoint, and focal length. However, Light Field data usually associates to a critical problem -the massive data. Therefore, compressing LF data is one of the main challenges in LF research. In this context, we present in this paper a comparative study for compressing LF data with not only the widely used image/video coding standards, such as JPEG-2000, H.264/AVC, HEVC and Google/VP9 but also with the most recent image/video coding solution, the Joint Exploration Model. In addition, this paper also proposes a LF image coding flow, which can be used as a benchmark for future LF compression evaluation. Finally, the compression efficiency of these coding solutions is thoroughly compared throughout a rich set of test conditions.

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HEVC-based light field image coding with bi-predicted self-similarity compensation

Abstract: HEVC-based light field image coding with bi-predicted self-similarity compensation.

Cited by 66 publications

References 8 publications

Objective and subjective evaluation of light field image compression algorithms

Objective and subjective evaluation of light field image compression algorithms

Light Field Compression With Homography-Based Low-Rank Approximation

Joint exploration model based light field image coding: A comparative study

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