Disparity based compression technique for focused plenoptic images

Choudhury, Chandrajit; Chaudhuri, Subhasis

doi:10.1145/2683483.2683510

Cited by 7 publications

(12 citation statements)

References 9 publications

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“…The proposed scheme is able to improve the RD compression performance when incorporated into the JPEG standard. In [241], [242], the disparity between adjacent micro-images is used to better reconstruct discarded microimages in the sparse set of micro-images. In [241], JPEG is used as the texture coder and lossless arithmetic coding is used for the disparity data.…”

Section: ) Dibr-based View Synthesismentioning

confidence: 99%

Dense Light Field Coding: A Survey

2020

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Light Field (LF) imaging is a promising solution for providing more immersive and closer to reality multimedia experiences to end-users with unprecedented creative freedom and flexibility for applications in different areas, such as virtual and augmented reality. Due to the recent technological advances in optics, sensor manufacturing and available transmission bandwidth, as well as the investment of many tech giants in this area, it is expected that soon many LF transmission systems will be available to both consumers and professionals. Recognizing this, novel standardization initiatives have recently emerged in both the Joint Photographic Experts Group (JPEG) and the Moving Picture Experts Group (MPEG), triggering the discussion on the deployment of LF coding solutions to efficiently handle the massive amount of data involved in such systems. Since then, the topic of LF content coding has become a booming research area, attracting the attention of many researchers worldwide. In this context, this paper provides a comprehensive survey of the most relevant LF coding solutions proposed in the literature, focusing on angularly dense LFs. Special attention is placed on a thorough description of the different LF coding methods and on the main concepts related to this relevant area. Moreover, comprehensive insights are presented into open research challenges and future research directions for LF coding. INDEX TERMS Camera array, image compression, light field, plenoptic, video compression.

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Section: ) Dibr-based View Synthesismentioning

confidence: 99%

Dense Light Field Coding: A Survey

2020

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“…Other coding schemes proposed to represent the LF data by a subsampled set of MIs with their associated disparity information [36]- [38]. As firstly proposed in [39], the grid of MIs is subsampled to remove the redundancy between neighboring MIs and to achieve compression.…”

Section: Disparity-assisted Lf Codingmentioning

confidence: 99%

“…Thus, only the remainder set of MIs and associated disparity are encoded and transmitted. At the decoder side, the LF data is reconstructed by simply applying a disparity shift (in [36], [38]) or by using a Depth Image Based Rendering (DIBR) algorithm modified to support the multiple MIs as input views (in [37]), and followed by an inpainting algorithm to fill in the missing areas. However, in real-world images, the disparity/depth information is estimated from the acquired LF raw data, which introduces some inaccuracies.…”

Section: Disparity-assisted Lf Codingmentioning

confidence: 99%

“…Additionally, due to occlusion problems and quantization errors when (lossy) encoding this disparity/depth maps, some synthesized MIs might present too many missing areas to be filled [37], thus introducing even further inaccuracies. Instead of uniformly selecting the MIs as in [38], the selection is performed adaptively in [36], [37], so as to obtain better view reconstruction.…”

Section: Disparity-assisted Lf Codingmentioning

confidence: 99%

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Light Field Coding With Field-of-View Scalability and Exemplar-Based Interlayer Prediction

Conti¹,

Soares²,

Nunes³

2018

IEEE Trans. Multimedia

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“…iii) LFC based on inter-view prediction [15][16][17][18][19][20] , in which a set of MIs, VIs or high resolution views are extracted and coded as a Pseudo-Video Sequence (PVS) or as multiview content; and iv) Disparity-assisted coding [21][22][23] , in which the disparity information is derived from the LF content and encoded along with a sparse set of texture information.…”

Section: Introductionmentioning

confidence: 99%

Weighted bi-prediction for light field image coding

Conti

Nunes

Soares

2017

Applications of Digital Image Processing XL

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Light field imaging based on a single-tier camera equipped with a microlens arrayalso known as integral, holoscopic, and plenoptic imaginghas currently risen up as a practical and prospective approach for future visual applications and services. However, successfully deploying actual light field imaging applications and services will require developing adequate coding solutions to efficiently handle the massive amount of data involved in these systems. In this context, selfsimilarity compensated prediction is a non-local spatial prediction scheme based on block matching that has been shown to achieve high efficiency for light field image coding based on the High Efficiency Video Coding (HEVC) standard. As previously shown by the authors, this is possible by simply averaging two predictor blocks that are jointly estimated from a causal search window in the current frame itself, referred to as self-similarity bi-prediction. However, theoretical analyses for motion compensated bi-prediction have suggested that it is still possible to achieve further rate-distortion performance improvements by adaptively estimating the weighting coefficients of the two predictor blocks. Therefore, this paper presents a comprehensive study of the rate-distortion performance for HEVC-based light field image coding when using different sets of weighting coefficients for self-similarity bi-prediction. Experimental results demonstrate that it is possible to extend the previous theoretical conclusions to light field image coding and show that the proposed adaptive weighting coefficient selection leads to up to 5 % of bit savings compared to the previous self-similarity bi-prediction scheme.

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Disparity based compression technique for focused plenoptic images

Cited by 7 publications

References 9 publications

Dense Light Field Coding: A Survey

Dense Light Field Coding: A Survey

Light Field Coding With Field-of-View Scalability and Exemplar-Based Interlayer Prediction

Weighted bi-prediction for light field image coding

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