JPEG Pleno Research Articles

Subjective performance evaluation of bitrate allocation strategies for MPEG and JPEG Pleno point cloud compression

The recent rise in interest in point clouds as an imaging modality has motivated standardization groups such as JPEG and MPEG to launch activities aiming at developing compression standards for point clouds. Lossy compression usually introduces visual artifacts that negatively impact the perceived quality of media, which can only be reliably measured through subjective visual quality assessment experiments. While MPEG standards have been subjectively evaluated in previous studies on multiple occasions, no work has yet assessed the performance of the recent JPEG Pleno standard in comparison to them. In this study, a comprehensive performance evaluation of JPEG and MPEG standards for point cloud compression is conducted. The impact of different configuration parameters on the performance of the codecs is first analyzed with the help of objective quality metrics. The results from this analysis are used to define three rate allocation strategies for each codec, which are employed to compress a set of point clouds at four target rates. The set of distorted point clouds is then subjectively evaluated following two subjective quality assessment protocols. Finally, the obtained results are used to compare the performance of these compression standards and draw insights about best coding practices.

Adaptive bridge model for compressed domain point cloud classification

The recent adoption of deep learning-based models for the processing and coding of multimedia signals has brought noticeable gains in performance, which have established deep learning-based solutions as the uncontested state-of-the-art both for computer vision tasks, targeting machine consumption, as well as, more recently, coding applications, targeting human visualization. Traditionally, applications requiring both coding and computer vision processing require first decoding the bitstream and then applying the computer vision methods to the decompressed multimedia signals. However, the adoption of deep learning-based solutions enables the use of compressed domain computer vision processing, with gains in performance and computational complexity over the decompressed domain approach. For point clouds (PCs), these gains have been demonstrated in the single available compressed domain computer vision processing solution, named Compressed Domain PC Classifier, which processes JPEG Pleno PC coding (PCC) compressed streams using a PC classifier largely compatible with the state-of-the-art spatial domain PointGrid classifier. However, the available Compressed Domain PC Classifier presents strong limitations by imposing a single, specific input size which is associated to specific JPEG Pleno PCC configurations; this limits the compression performance as these configurations are not ideal for all PCs due to their different characteristics, notably density. To overcome these limitations, this paper proposes the first Adaptive Compressed Domain PC Classifier solution which includes a novel adaptive bridge model that allows to process the JPEG Pleno PCC encoded bit streams using different coding configurations, now maximizing the compression efficiency. Experimental results show that the novel Adaptive Compressed Domain PC Classifier allows JPEG PCC to achieve better compression performance by not imposing a single, specific coding configuration for all PCs, regardless of its different characteristics. Moreover, the added adaptability power can achieve slightly better PC classification performance than the previous Compressed Domain PC Classifier and largely better PC classification performance (and lower number of weights) than the PointGrid PC classifier working in the decompressed domain.

Super-Rays Grouping Scheme and Novel Coding Architecture for Computational Time Reduction of Graph-Based Light Field Coding

Graph-based Light Field coding using the concept of super-rays is powerful to exploit signal redundancy along irregular shapes and achieves good energy compaction, compared to rectangular block -based approaches. However, its main limitation lies in the high time complexity for eigen-decomposition of each super-ray local graph, a high number of which can be found in a Light Field when segmented into super-rays. This paper examines a grouping scheme for super-rays in order to reduce the number of eigen-decomposition times, and proposes a novel coding architecture to handle the signal residual data arising for each super-ray group, as a tradeoff to achieve lower computational time. Experimental results have shown to reduce a considerable amount of decoding time for Light Field scenes, despite having a slight increase in the coding bitrates when compared with the original non-grouping super-ray-based approach. The proposal also remains to have competitive performance in Rate Distortion in comparison to HEVC-based and JPEG Pleno -based methods.

JPEG Pleno holography presents the numerical reconstruction software for holograms: an excursion in holographic views.

Digital reconstructions of numerical holograms enable data visualization and serve a multitude of purposes ranging from microscopy to holographic displays. Over the years, many pipelines have been developed for specific hologram types. Within the standardization effort of JPEG Pleno holography, an open-source MATLAB toolbox was developed that reflects the best current consensus. It can process Fresnel, angular spectrum, and Fourier-Fresnel holograms with one or more color channels; it also allows for diffraction-limited numerical reconstructions. The latter provides a way to reconstruct holograms at their intrinsic physical instead of an arbitrarily chosen numerical resolution. The Numerical Reconstruction Software for Holograms v10 supports all large public data sets featured by UBI, BCOM, ETRI, and ETRO, in their native and vertical off-axis binary forms. Through the release of this software, we hope to improve the reproducibility of research, thus enabling consistent comparison of data between research groups and the quality of specific numerical reconstructions.

Supporting Wider Baseline Light Fields in JPEG Pleno With a Novel Slanted 4D-DCT Coding Mode

Light fields are one of the emerging 3D representation formats with an effective potential to offer very realistic and immersive visual experiences. This capability comes at the cost of a very large amount of acquired data which practical use requires efficient coding solutions. This need was already addressed by the JPEG Pleno Light Field Coding standard for static light fields, which has specified two coding modes, named 4D-Transform and 4D-Prediction. While the first offers better compression performance for smaller baseline light fields, the second excels for larger baseline light fields. This paper intends to propose a novel light field coding mode, the Slanted 4D-Transform coding mode, which extends the 4D-Transform coding mode based on the conventional 4D-DCT, to offer better compression performance than both the available JPEG Pleno coding modes, independently of the baseline. The key idea is to apply first to each 4D block in the light field an adaptive, hierarchical geometric transformation, which makes the data in the block more energy-compaction friendly for the following 4D-DCT. The rate-distortion performance results show that the proposed Slanted 4D-Transform codec is able to outperform both the already standardized JPEG Pleno coding modes, showing BD-Rates gains of 31.03% and 28.30% for the 4D-Transform and 4D-Prediction modes, respectively, thus implying that a single coding mode can efficiently code all types of light fields.

JPEG Column: 97th JPEG Meeting

The 97th JPEG meeting was held online from 24 to 28 October 2022. JPEG received responses to the Call for Proposals (CfP) on JPEG Fake Media, the first multimedia international standard designed to facilitate the secure and reliable annotation of media assets creation and modifications. In total six responses were received addressing different requirements in the scope of this standardization initiative. Moreover, relevant advances were made on the standardization of learning-based coding, notably the learning-based coding of images, JPEG AI, and JPEG Pleno point cloud coding. Furthermore, the explorations on quality assessment of images, JPEG AIC, and of JPEG Pleno light field had relevant advances with the definition of their Calls for Contributions and Common Test Conditions.

JPEG Column: 96th JPEG Meeting

The 96 th JPEG meeting was held online from 25 to 29 July 2022. The meeting was one of the most productive in the recent history of JPEG with the analysis of the responses of two Calls for Proposals (CfP) for machine learning-based coding solutions, notably JPEG AI and JPEG Pleno Point Cloud Coding. The superior performance of the CfP responses compared to the state-of-the-art anchors leave little doubt about the future of coding technologies becoming dominated by machine learning-based solutions with the expected consequences on the standardisation pathway. A new era of multimedia coding standardisation has begun. Both activities had defined a verification model, and are pursuing a collaborative process that will select the best technologies for the definition of the new machine learning-based standards.

JPEG Column: 91st JPEG Meeting

The 91st JPEG meeting was held online from 19 to 23 April 2021. This meeting saw several activities relating to holographic coding, notably the release of the JPEG Pleno Holography Call for Proposals, consolidated with the definition of the use cases and requirements for holographic coding and common test conditions that will assure the evaluation of the future proposals.

JPEG Column: 90th JPEG Meeting

The 90th JPEG meeting was held online from 18 to 22 January 2021. This meeting was distinguished by very relevant activities, notably the new JPEG AI standardization project planning, and the analysis of the Call for Evidence on JPEG Pleno Point Cloud Coding.

Lossless Compression of Plenoptic Camera Sensor Images

We propose a codec for the lossless compression of plenoptic camera sensor images. The proposed encoder starts by splitting the input lenslet image into rectangular patches, with each patch corresponding to a microlens image. The encoder and decoder exploit the correlation between the pixels in neighbor patches using a patch-by-patch prediction mechanism where each pixel of a patch has its own dedicated sparse predictor designed to utilize the most relevant pixels from the neighbor patch to the left. An intra-patch prediction mask together with the pixels from the neighbor left patch form the final prediction template. The encoder performs the design of sparse predictors by first finding the relevant regressors in the final template. The patches are classified into M classes according to two possible mechanisms (either based on depth information or based on Bayer mask colors), and the sparse predictor design is performed for each pair (class label; patch pixel index). A relevant context selection mirrors the selection of relevant regressors, thereby providing the arithmetic coding with skewed coding distributions at each context. We show examples of the application of the proposed methods on sensor images from the JPEG Pleno database, thus demonstrating the improved performance of the proposed methods compared to the existing predictive methods for encoding camera sensor images.

Silhouette 4D With Context Selection: Lossless Geometry Compression of Dynamic Point Clouds

Recently we have proposed a coding algorithm of point cloud geometry based on a rather different approach from the popular octree representation. In our algorithm, the point cloud is decomposed in silhouettes, hence the name Silhouette Coder, and context adaptive arithmetic coding is used to exploit redundancies within the point cloud (intra frame coding), and also using a reference point cloud (inter frame coding). In this letter we build on our previous work and propose a context selection algorithm as a pre-processing stage. With this algorithm, the point cloud is first parsed testing a large number of candidate context locations. The algorithm selects a small number of these contexts that better reflect the current point cloud, and then encode it with this choice. The proposed method further improves the results of our previous coder, Silhouette 4D, by $10 \%$ , on average, on a dynamic point cloud dataset of the JPEG Pleno, and achieves bitrates competitive with some high quality lossy coders such as the MPEG G-PCC.

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

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.

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

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.

Local low rank approximation with a parametric disparity model for light field compression.

We address the problem of light field dimensionality reduction for compression. We describe a local low rank approximation method using a parametric disparity model. The local support of the approximation is defined by super-rays. A super-ray can be seen as a set of super-pixels that are coherent across all light field views. A dedicated super-ray construction method is first described that constrains the super-pixels forming a given super-ray to be all of the same shape and size, dealing with occlusions. This constraint is needed so that the super-rays can be used as supports of angular dimensionality reduction based on low rank matrix approximation. The light field low rank assumption depends on how much the views are correlated, i.e. on how well they can be aligned by disparity compensation. We first introduce a parametric model describing the local variations of disparity within each super-ray. We then consider two methods for estimating the model parameters. The first method simply fits the model on an input disparity map. We then introduce a disparity estimation method using a low rank prior. This method alternatively searches for the best parameters of the disparity model and of the low rank approximation. We assess the proposed disparity parametric model, first assuming that the disparity is constant within a super-ray, and second by considering an affine disparity model. We show that using the proposed disparity parametric model and estimation algorithm gives an alignment of super-pixels across views that favours the low rank approximation compared with using disparity estimated with classical computer vision methods. The low rank matrix approximation is computed on the disparity compensated super-rays using a singular value decomposition (SVD). A coding algorithm is then described for the different components of the proposed disparity-compensated low rank approximation. Experimental results show performance gains, with a rate saving going up to 92.61%, compared with the JPEG Pleno anchor, for real light fields captured by a Lytro Illum camera. The rate saving goes up to 37.72% with synthetic light fields. The approach is also shown to outperform an HEVC-based light field compression scheme.

Light Field Image Coding Based on Hybrid Data Representation

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-of-the-art benchmarks. For lenslet light field content, the proposed coding algorithm outperforms the HEVC-based 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 HEVC-based benchmark, as well as MuLE and WaSP.

Geometry-Aware Graph Transforms for Light Field Compact Representation.

The paper addresses the problem of energy compaction of dense 4D light fields by designing geometry-aware local graph-based transforms. Local graphs are constructed on super-rays that can be seen as a grouping of spatially and geometry-dependent angularly correlated pixels. Both non separable and separable transforms are considered. Despite the local support of limited size defined by the super-rays, the Laplacian matrix of the non separable graph remains of high dimension and its diagonalization to compute the transform eigen vectors remains computationally expensive. To solve this problem, we then perform the local spatio-angular transform in a separable manner. We show that when the shape of corresponding super-pixels in the different views is not isometric, the basis functions of the spatial transforms are not coherent, resulting in decreased correlation between spatial transform coefficients. We hence propose a novel transform optimization method that aims at preserving angular correlation even when the shapes of the super-pixels are not isometric. Experimental results show the benefit of the approach in terms of energy compaction. A coding scheme is also described to assess the rate-distortion perfomances of the proposed transforms and is compared to state of the art encoders namely HEVC-lozenge [1], JPEG pleno 1.1 [2], HEVC-pseudo [3] and HLRA [4].

Phase-shifting digital holographic data compression

Modern holography for 3D imaging allows to reconstruct all the parallaxes that are needed for a truly immersive visualization. Nevertheless, it possesses huge amount of data which induces higher transmission and storage requirements. To gain more popularity and acceptance, digital holography demands development of efficient coding schemes that provide significant data compression at low computation cost. Another issue that needs to be tackled when designing holography coding algorithms is interoperability with commonly used formats. In light of this, the upcoming JPEG Pleno standard aims to develop a standard framework for the representation and exchange of new imaging modalities such as holographic imaging while maintaining backward compatibility with the legacy JPEG decoders. This paper summarizes the early work on lossy compression of computer graphic holograms and analyses the efficiency of additional methods that may exhibit good satisfactory coding performance while considering the backward compatibility with legacy JPEG decoders. To validate our findings, the results of our tests are shown and interpreted. Finally, we also outline the emerging trends for future researches.

Guest Editorial Immersive Video Coding and Transmission

Immersive media are gaining in popularity today, and significant efforts are being undertaken in academia and industry to explore its immanent new scientific and technological challenges. There are significant activities in industry and standardization to provide enablers for production, coding, transmission, and consumption of this type of media and the new user experiences it enables. In terms of standardization, the topic has triggered multiple activities in the areas of systems, 3D graphics, audio, image, and video. The technological roadmap foresees an evolution from consumption of the visual media with three degrees of freedom (3DoF), i.e. the ability to look around at a fixed viewing position in an observed scene) to 3DoF+, enabling further limited modifications of the viewing position, and to different variants of six degrees of freedom (6DoF), allowing the user not only to look around but also to move around in the observed scene. Different terminology is used in various communities, referring to immersive or omnidirectional media, virtual reality (VR), or 360° video. While the coded representation of audio-visual media for 6DoF remains a field of active research, 3DoF technologies have sufficient maturity to progress towards specification in near-term standards and recommendations. At the video codec level, this includes coding of 2D and 3D virtual reality (VR) / 360° content using the HEVC standard (Rec. ITU-T H.265 | ISO/IEC 23008-2) as the initial step, with new Supplemental Enhancement Information (SEI) messages for omnidirectional video. For storage and delivery, the Omnidirectional Media Format (OMAF) (ISO/IEC 23090-2) provides a set of consistent enablers for download and over-the-top streaming of 3DoF content. At the same time, immersive still image coding formats are currently developed in JPEG Pleno.

JPEG Pleno: Providing representation interoperability for holographic applications and devices

JPEG Pleno: Providing representation interoperability for holographic applications and devices

Base-Anchored Model for Highly Scalable and Accessible Compression of Multiview Imagery

We present a compression scheme for multiview imagery that facilitates high scalability and accessibility of the compressed content. Our scheme relies upon constructing at a single base view, a disparity model for a group of views, and then utilizing this base-anchored model to infer disparity at all views belonging to the group. We employ a hierarchical disparity-compensated inter-view transform where the corresponding analysis and synthesis filters are applied along the geometric flows defined by the base-anchored disparity model. The output of this inter-view transform along with the disparity information is subjected to spatial wavelet transforms and embedded block-based coding. Rate-distortion results reveal superior performance to the x.265 anchor chosen by the JPEG Pleno standards activity for the coding of multiview imagery captured by high-density camera arrays.