A 4D DCT-Based Lenslet Light Field Codec

Carvalho, Murilo B. de; Pereira, Marcio P.; Alves, Gustavo; Silva, Eduardo A. B. da; Pagliari, Carla L.; Pereira, Fernando; Testoni, Vanessa

doi:10.1109/icip.2018.8451684

Cited by 48 publications

(40 citation statements)

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“…In [89], a so-called Multidimensional Light field Encoder using 4D Transforms and Hexadeca-trees (MuLE-TH) LLF image coding solution is proposed, where the intrinsic spatial-angular or 4D LLF redundancy is exploited as a whole by means of a 4D transform, see architecture in Figure 6. The key idea is to take advantage of a tree data structure based transform coefficients' bitplanes decomposition to attain a more efficient compression.…”

Section: -Dimensional Array Of Images Based Llficmentioning

confidence: 99%

“…Differently from the coding solution described in Section IV.B, which exploits the inter-PI and intra-PI correlations separately and using different tools, the MuLE-TH codec exploits the intrinsic 4D LLF correlation as a whole using the 4D-DCT, which may allow a more efficient exploitation of the redundancy. The MuLE-TH codec has meanwhile been improved [5] regarding in initial description [89], both in terms of compression efficiency and random access capability, notably by adopting RD-optimized 4D block partitioning and quantization strategies, and independent coding of each 4D block; 10-bit YUV 4:4:4 PIs are supported at the encoder input. The improved MuLE-TH codec, hereafter called MuLE, has been adopted as the 4D transform mode in the JPEG Pleno standard, due to its good performance in terms of compression efficiency and random access for LLF content [99].…”

Section: -Dimensional Array Of Images Based Llficmentioning

confidence: 99%

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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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Section: -Dimensional Array Of Images Based Llficmentioning

confidence: 99%

Section: -Dimensional Array 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 [29], the authors employ compressive sensing and convolutional neural networks to code a LF as a 4D tensor. Studies on the sparsity of the 4D-DCT coefficients associated to a LF [30], [31] led to the development of a simple LF codec based on the separable 4D-DCT [32], referred to as MuLE (Multidimensional Light Field Encoder); this codec shows good RD performance when there is large inter-view redundancy as for lenslet LFs and it is an early version of the LF codec taken as the main focus of this paper, the JPEG Pleno Light Field Coding 4D-Transform mode.…”

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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“…De Carvalho et at. propose the adoption of 4D DCT to obtain a compact representation of the light field structure [9]. The DCT coefficients are grouped using hexadecatrees, for each bitplane, and encoded using an arithmetic encoder.…”

Section: Introductionmentioning

confidence: 99%

Light field compression using translation-assisted view estimation

Heriard-Dubreuil

Viola

Ebrahimi

2019

2019 Picture Coding Symposium (PCS)

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Light field technology has recently been gaining traction in the research community. Several acquisition technologies have been demonstrated to properly capture light field information, and portable devices have been commercialized to the general public. However, new and efficient compression algorithms are needed to sensibly reduce the amount of data that needs to be stored and transmitted, while maintaining an adequate level of perceptual quality. In this paper, we propose a novel light field compression scheme that uses view estimation to recover the entire light field from a small subset of encoded views. Experimental results on a widely used light field dataset show that our method achieves good coding efficiency with average rate savings of 54.83% with respect to HEVC. Index Terms-light field compression, view estimation, light field coding This work has been conducted in the framework of projects "Light field Image and Video coding and Evaluation" and "Advanced Visual Representation and Coding in Augmented and Virtual Reality

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A 4D DCT-Based Lenslet Light Field Codec

Cited by 48 publications

References 10 publications

Lenslet Light Field Image Coding: Classifying, Reviewing and Evaluating

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

Light field compression using translation-assisted view estimation

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