Performance analysis of JPEG Pleno light field coding

Perra, Cristian; Astola, Pekka; Silva, Eduardo A. B. da; Khanmohammad, Hesam; Pagliari, Carla L.; Schelkens, Peter; Tăbuş, Ioan

doi:10.1117/12.2528391

Cited by 16 publications

(34 citation statements)

References 7 publications

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“…MuLE-MTH shows competitive RD performance when compared to more sophisticated LF coding solutions, with the advantage of being conceptually simple and royalty-free, not relying on depth maps and offering block-based random access. Due to these advantages, MuLE-MTH has been selected as one of the two coding modes specified in the JPEG Pleno Light Field Coding standard, specifically targeting densely angular sampled LFs such as lenslet LFs [36], [37], [40].…”

Section: Discussionmentioning

confidence: 99%

“…For the results presented above, while the best MuLE-TSR and MuLE-TSB configurations use 4D block dimensions of (t, s, v, u) = (13,13,31,25), MuLE-MTH uses a larger block dimension as the block may be dynamically segmented depending on its content. These RD performance results clearly show why MuLE-MTH was accepted for inclusion in the JPEG Pleno Light Field Coding standard as the 4D-Transform Mode [36], [37], [40]. The JPEG Pleno Verification Model software includes the MuLE-MTH codec software and is available at [51].…”

Section: Mule-mth Rd Performance Assessmentmentioning

confidence: 96%

“…This is the LF codec which design progress will be deeply motivated, described, analyzed and assessed in this paper. References [37], [40] and Section VII of this paper compare the RD performance of these two standard LF codecs for densely angular sampled LFs since this is the target LF content for the 4D-Transform coding mode, which is the subject of this paper. JPEG Pleno Part 1 [41], named Framework, defines a very flexible file format, named JPL (Jpeg PLeno), which specifies the signalling syntax for detailed information and contextualization of the streams associated to the plenoptic imaging content streams encapsulated in the file.…”

Section: B Jpeg Pleno Light Field Coding Standardmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Mule-mth Rd Performance Assessmentmentioning

confidence: 96%

Section: B Jpeg Pleno Light Field Coding Standardmentioning

confidence: 99%

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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 key technologies are 4D transform and 4D prediction. For the performance of the JPEG Pleno, please see [75]. One example point on the R-D curve is PSNR (Peak Signal to Noise Ratio) 38 dB at rate 0.1 bpp (bits per pixel).…”

Section: Data Compression and Coding Of The Integral Imaging Datamentioning

confidence: 99%

Roadmap on 3D integral imaging: sensing, processing, and display

et al. 2020

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This Roadmap article on three-dimensional integral imaging provides an overview of some of the research activities in the field of integral imaging. The article discusses various aspects of the field including sensing of 3D scenes, processing of captured information, and 3D display and visualization of information. The paper consists of a series of 15 sections from the experts presenting various aspects of the field on sensing, processing, displays, augmented reality, microscopy, object recognition, and other applications. Each section represents the vision of its author to describe the progress, potential, vision, and challenging issues in this field.

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“…This property to some extent differentiates WaSP from many other light field coding algorithms, as explained in Section I-C, with several of the algorithms designed to be efficient or practical with only one type of light field data. After being published to the JPEG Pleno Light Field work group in the 80th JPEG meeting in Berlin, WaSP was selected as the verification model (VM) software for VM 1.0 [10], VM 1.1 [11] and as the 4D prediction mode in VM 2.1 [12], all which have been used by the research community for evaluating the performance of light field coding algorithms as will be further discussed in Section I-C. For further discussion of JPEG Pleno light field coding, we refer the reader to [13] and [14].…”

Section: Introductionmentioning

confidence: 99%

Coding of Light Fields Using Disparity-Based Sparse Prediction

Astola

Tăbuş

2019

IEEE Access

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In this paper we introduce a new light field codec, dubbed WaSPR (warping and sparse prediction on regions), which has additional features and improved performance when compared to our recently introduced WaSP (warping and sparse prediction) codec. We present in detail the overall scheme, including the initial WaSP structure, and the additional new features of WaSPR, including a region based sparse prediction stage, and the addition of a more efficient way to encode the prediction residuals by including separate inter-view coding of residuals obtained at each hierarchical level. The new scheme is demonstrated to improve over both modes of the JPEG Pleno Verification Model software version 2.1, and is shown to provide competitive results when compared against several recently published light field codecs.INDEX TERMS Light field coding, image coding, plenoptic, multi-view.

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Performance analysis of JPEG Pleno light field coding

Cited by 16 publications

References 7 publications

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

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

Roadmap on 3D integral imaging: sensing, processing, and display

Coding of Light Fields Using Disparity-Based Sparse Prediction

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