WaSP: Hierarchical Warping, Merging, and Sparse Prediction for Light Field Image Compression

Astola, Pekka; Tăbuş, Ioan

doi:10.1109/euvip.2018.8611756

Cited by 32 publications

(47 citation statements)

References 13 publications

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“…Compared to other LF coding solutions in the literature (outside the transform-based category), it has been shown [165] that the transform-based solutions [125] can achieve competitive RD performance when compared against LF coding solutions based on inter-view prediction (such as PVS-based approaches) and some solutions based on view synthesis [158], [159], but only for coding LF acquired using lenslet cameras, where the 4D redundancy is considerably larger than in LF acquired using multi-camera or gantry setups.…”

Section: ) Discussionmentioning

confidence: 99%

“…The segmentation map of the reference subaperture image and the disparity value per super-ray are also encoded using an arithmetic coder and transmitted to the decoder side. Experimental results for lenslet images captured by a Lytro Illum camera (including three LF images from the EPFL LF Dataset in Table 1) are shown, in which the proposed solution is compared against four different LF coding solutions: i) a PVS-based solution using lozenge scan order and HEVC; ii) a PVS-based solution proposed in [157] (see Section II-B2); iii) a LF coding solution based on view synthesis proposed in [158], [159] (see Section III-D1); and iv) a LF coding solution based on view synthesis proposed in [49] (see Section III-D3). From these results, it is seen that the proposed solution is outperformed by the PVS-based solution in [157] and by the view synthesis-based solution in [49], but is able to present some coding gains at high bit rates when compared to the PVS-based with lozenge scan and the views synthesis-based solution in [158], [159].…”

Section: ) Gft-based Codingmentioning

confidence: 99%

“…This solution was recognized as the most innovative algorithm at the ICIP 2017 grand challenge on LF compression [24]. In [158], [159], the authors propose to extend their previous solution in [126] for coding LF content acquired using a multi-camera array. For this, a coding scheme similar to the one used in [126] is adopted.…”

Section: ) Dibr-based View Synthesismentioning

confidence: 99%

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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: ) Discussionmentioning

confidence: 99%

Section: ) Gft-based Codingmentioning

confidence: 99%

Section: ) Dibr-based View Synthesismentioning

confidence: 99%

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Dense Light Field Coding: A Survey

2020

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“…In [94], a so-called Warping and Sparse Prediction (WaSP) based LLFIC solution is proposed, which is based on a layered arrangement of the full set of PIs (rendered as described in Section II), see encoding architecture in Figure 7. The key idea is to predictively encoded each PI layer from (previously encoded) PIs belonging to lower layers, thus exploiting the correlation between neighboring (or nearby) PIs while providing random access.…”

Section: N-layered Sets Of Images Based Llficmentioning

confidence: 99%

Lenslet Light Field Image Coding: Classifying, Reviewing and Evaluating

Brites

Ascenso

Pereira

2021

IEEE Trans. Circuits Syst. Video Technol.

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In recent years, visual sensors have been quickly improving, notably targeting richer acquisitions of the light present in a visual scene. In this context, the so-called lenslet light field (LLF) cameras are able to go beyond the conventional 2D visual acquisition models, by enriching the visual representation with directional light measures for each pixel position. LLF imaging is associated to large amounts of data, thus critically demanding efficient coding solutions in order applications involving transmission and storage may be deployed. For this reason, considerable research efforts have been invested in recent years in developing increasingly efficient LLF imaging coding (LLFIC) solutions. In this context, the main objective of this paper is to review and evaluate some of the most relevant LLFIC solutions in the literature, guided by a novel classification taxonomy, which allows better organizing this field. In this way, more solid conclusions can be drawn about the current LLFIC status quo, thus allowing to better drive future research and standardization developments in this technical area.Index Terms-Light field image coding, lenslet, taxonomy, perspective image, micro-image. II. LLF IMAGING: A BRIEF REVIEWLLF imaging is a 3D visual representation model where the scene's light radiance is captured through a high-density set of

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“…While the first subset is coded as a video sequence using HEVC [21], the remaining subsets are iteratively estimated using Fourier Disparity Layer representation and the prediction residual coded with HEVC; in this solution, both depth map estimation and view synthesis techniques play important roles. In [35], a depth-based LF codec named Warping and Sparse Prediction (WaSP) is proposed; an improved version of this codec has become the 4D-Prediction mode of the JPEG Pleno Light Field Coding standard [10], [36], [37]. The WaSP codec is based on depth-based warping and warped reference views merging, which build the main prediction stage.…”

Section: A Most Relevant Light Field Coding Solutionsmentioning

confidence: 99%

The JPEG Pleno Light Field Coding Standard 4D-Transform Mode: How to Design an Efficient 4D-Native Codec

et al. 2020

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The increasing demand for highly realistic and immersive visual experiences has led to the emergence of richer 3D visual representation models such as light fields, point clouds and meshes. Light fields may be modelled as a 2D array of 2D views, corresponding to a large amount of data, which demands for highly efficient coding solutions. Although static light fields are inherently 4D structures, the coding solutions in the literature mostly employ traditional 2D coding tools associated with techniques such as depth-based image rendering to generate a residual, again to be coded using available 2D coding tools. To address the market needs, JPEG has launched the so-called JPEG Pleno standard, which Part 2 is dedicated to light field coding. The JPEG Pleno Light Field Coding standard includes two coding modes, one based on the 4D-DCT, so-called 4D-Transform mode, and another based on depth-based synthesis, so-called 4D-Prediction. The 4D-Transform coding mode standardizes for the first time a 4D-native light field coding solution where the full light field redundancy across the four dimensions is comprehensively exploited, somehow extending to 4D the 2D coding framework adopted decades ago by the popular JPEG Baseline standard. In this context, this paper describes and analyzes in detail the conceptual and algorithmic design process which has led to the creation of the JPEG Pleno Light Field Coding standard 4D-Transform coding mode. This has happened through a sequence of steps involving technical innovation design and integration where increasingly sophisticated coding tools have been combined and improved to maximize the final rate distortion (RD) performance. INDEX TERMS JPEG Pleno standard, Light field coding, 4D-Transform mode, 4D-DCT.

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WaSP: Hierarchical Warping, Merging, and Sparse Prediction for Light Field Image Compression

Cited by 32 publications

References 13 publications

Dense Light Field Coding: A Survey

Dense Light Field Coding: A Survey

Lenslet Light Field Image Coding: Classifying, Reviewing and Evaluating

The JPEG Pleno Light Field Coding Standard 4D-Transform Mode: How to Design an Efficient 4D-Native Codec

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