Light field HEVC-based image coding using locally linear embedding and self-similarity compensated prediction

Monteiro, Ricardo J. S.; Lucas, Luı́s; Conti, Caroline; Nunes, Paulo; Rodrigues, Nuno M. M.; Faria, Sérgio M. M. de; Pagliari, Carla L.; Silva, Eduardo A. B. da; Soares, Luís Ducla

doi:10.1109/icmew.2016.7574670

Cited by 60 publications

(42 citation statements)

References 10 publications

(18 reference statements)

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“…The best among the two is selected for selfsimilarity estimation. The same approach is used in [5], which integrates self- similarity compensated prediction in HEVC Intra coding, and additionally implements locally linear embedding to further improve the compression performance. Locally linear embedding estimates the current block by solving a least-squares optimization problem to find the best linear combination of k nearest neighbors in a casual search window.…”

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)

View full text Add to dashboard Cite

show abstract

“…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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“…More recently, five proposals for lenslet image compression have been collected within the ICME 2016 Grand Challenge. Three proposals relied on improving HEVC intra compression efficiency by exploiting the redundancies in the lenslet image [23], [24], [25], whereas the rest used pseudo-temporal sequences to code the lenslet images [26], [27].…”

Section: B Light Field Image Compressionmentioning

confidence: 99%

“…To perform the compression, the software x265 was used 3 . The second algorithm P 02 uses a modified version of HEVC intra profile, which integrates Locally Linear Embedding (LLE) and Self Similarity (SS) to exploit the redundancies in the lenslet structure [25]. The image is partitioned into blocks using HEVC intra prediction scheme.…”

Section: A Lenslet Image Compressionmentioning

confidence: 99%

Comparison and Evaluation of Light Field Image Coding Approaches

Viola

Řeřábek

Ebrahimi

2017

IEEE J. Sel. Top. Signal Process.

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Abstract-The recent advances in light field imaging, supported among others by the introduction of commercially available cameras e.g. Lytro or Raytrix, are changing the ways in which visual content is captured and processed. Efficient storage and delivery systems for light field images must rely on compression algorithms. Several methods to compress light field images have been proposed recently. However, in-depth evaluations of compression algorithms have rarely been reported. This paper aims at evaluation of perceived visual quality of light field images and at comparing the performance of a few state of the art algorithms for light field image compression. First, a processing chain for light field image compression and decompression is defined for two typical use cases, professional and consumer. Then, five light field compression algorithms are compared by means of a set of objective and subjective quality assessments. An interactive methodology recently introduced by authors, as well as a passive methodology is used to perform these evaluations. The results provide a useful benchmark for future development of compression solutions for light field images.

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Light field HEVC-based image coding using locally linear embedding and self-similarity compensated prediction

Cited by 60 publications

References 10 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

Comparison and Evaluation of Light Field Image Coding Approaches

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