Content-Based Light Field Image Compression Method With Gaussian Process Regression

Liu, Deyang; An, Ping; Ma, Ran; Zhan, Wei; Yahya, Ali Abdullah

doi:10.1109/tmm.2019.2934426

Cited by 49 publications

(29 citation statements)

References 36 publications

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“…Most of the solutions in the first category aim at adding extra prediction modes in existing standards to capture the spatio-angular correlation. This is for example the case in [17]- [22] where the authors use block-based self-similarity techniques, or macro-pixel directional prediction modes as in [23], as additional HEVC Intra prediction modes. Scalable extensions of HEVC have also been considered, e.g., as in [24] where a sparse set of micro-lens images is encoded in a base layer.…”

Section: A Light Field Compressionmentioning

confidence: 99%

Rate-Distortion Optimized Graph Coarsening and Partitioning for Light Field Coding

Rizkallah

Maugey

Guillemot

2021

IEEE Trans. on Image Process.

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Graph-based transforms are powerful tools for signal representation and energy compaction. However, their use for high dimensional signals such as light fields poses obvious problems of complexity. To overcome this difficulty, one can consider local graph transforms defined on supports of limited dimension, which may however not allow us to fully exploit long-term signal correlation. In this paper, we present methods to optimize local graph supports in a rate distortion sense for efficient light field compression. A large graph support can be well adapted for compression efficiency, however at the expense of high complexity. In this case, we use graph reduction techniques to make the graph transform feasible. We also consider spectral clustering to reduce the dimension of the graph supports while controlling both rate and complexity. We derive the distortion and rate models which are then used to guide the graph optimization. We describe a complete light field coding scheme based on the proposed graph optimization tools. Experimental results show rate-distortion performance gains compared to the use of fixed graph support. The method also provides competitive results when compared against HEVC-based and the JPEG Pleno light field coding schemes. We also assess the method against a homography-based low rank approximation and a Fourier disparity layer based coding method.

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Section: A Light Field Compressionmentioning

confidence: 99%

Rate-Distortion Optimized Graph Coarsening and Partitioning for Light Field Coding

Rizkallah

Maugey

Guillemot

2021

IEEE Trans. on Image Process.

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“…In [18], the plenoptic image is partitioned into tiles, and the sequence of tiles is then compressed using HEVC. The authors of [19] classify the HEVC prediction units (PU) in 3 different categories based on texture homogeneity and use a different prediction mode, based on a Gaussian process regression, for each texture category.…”

Section: Related Workmentioning

confidence: 99%

Local Low Rank Approximation With a Parametric Disparity Model for Light Field Compression

Dib

Pendu

Jiang

et al. 2020

IEEE Trans. on Image Process.

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“…The prediction from the N templates is modeled as a non-linear gaussian process and gaussian process regression is used for estimating the prediction block. More recently in order to improve de prediction accuracy for non-homogenous textures and to reduce the computational complexity in this work, in [30], the authors proposed to apply a classification method that can segment the non-homogeneous texture areas improving the prediction accuracy. Moreover, the computational complexity is improved by using different prediction modes for each specific area of the lenslet LF image, i.e., content-based prediction.…”

Section: B Mi-based Related Workmentioning

confidence: 99%

Light Field Image Coding Based on Hybrid Data Representation

et al. 2020

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This paper proposes a novel efficient light field coding approach based on a hybrid data representation. Current state-of-the-art light field coding solutions either operate on micro-images or sub-aperture images. Consequently, the intrinsic redundancy that exists in light field images is not fully exploited, as is demonstrated. This novel hybrid data representation approach allows to simultaneously exploit four types of redundancies: i) sub-aperture image intra spatial redundancy, ii) sub-aperture image inter-view redundancy, iii) intra-micro-image redundancy, and iv) inter-micro-image redundancy between neighboring micro-images. The proposed light field coding solution allows flexibility for several types of baselines, by adaptively exploiting the most predominant type of redundancy on a coding block basis. To demonstrate the efficiency of using a hybrid representation, this paper proposes a set of efficient pixel prediction methods combined with a pseudo-video sequence coding approach, based on the HEVC standard. Experimental results show consistent average bitrate savings when the proposed codec is compared to relevant state-ofthe-art benchmarks. For lenslet light field content, the proposed coding algorithm outperforms the HEVCbased pseudo-video sequence coding benchmark by an average bitrate savings of 23%. It is shown for the same light field content that the proposed solution outperforms JPEG Pleno verification models MuLE and WaSP, as these codecs are only able to achieve 11% and −14% bitrate savings over the same HEVC-based benchmark, respectively. The performance of the proposed coding approach is also validated for light fields with wider baselines, captured with high-density camera arrays, being able to outperform both the HEVCbased benchmark, as well as MuLE and WaSP.INDEX TERMS Light field representation, light field image coding, HEVC, pseudo-video sequence, spatial pixel prediction, least squares prediction.

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Content-Based Light Field Image Compression Method With Gaussian Process Regression

Cited by 49 publications

References 36 publications

Rate-Distortion Optimized Graph Coarsening and Partitioning for Light Field Coding

Rate-Distortion Optimized Graph Coarsening and Partitioning for Light Field Coding

Local Low Rank Approximation With a Parametric Disparity Model for Light Field Compression

Light Field Image Coding Based on Hybrid Data Representation

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