Assessing objective quality metrics for JPEG and MPEG point cloud coding

As applications using immersive media gain increasing attention from both academia and industry, research in the field of point cloud compression has greatly intensified in recent years, leading to the development of the MPEG compression standards V-PCC and G-PCC, as well as the more recent JPEG Pleno learning-based point cloud coding. Each of the above-mentioned standards is based on a different algorithm, introducing distinct types of degradation that may impair the quality of experience when high lossy compression is applied. Although the impact on perceptual quality could be accurately evaluated during subjective quality assessment experiments, objective quality metrics serve as predictors of the visually perceived quality and provide similarity scores without human intervention. Nevertheless, their accuracy can be susceptible to the characteristics of the evaluated media as well as to the type and intensity of the added distortion. While the performance of multiple state-of-the-art objective quality metrics has already been evaluated through their correlation with subjective scores obtained in the presence of artifacts produced by the MPEG standards, no study has evaluated how metrics perform with the more recent JPEG Pleno point cloud coding. In this paper, a study is conducted to benchmark the performance of a large set of objective quality metrics in a subjective dataset including distortions produced by JPEG and MPEG codecs. The dataset also contains three different trade-offs between color and geometry compression for each codec, adding another dimension to the analysis. Performance indexes are computed over the entire dataset but also after splitting according to the codec and to the original model, resulting in detailed insights about the overall performance of each visual quality predictor as well as their cross-content and cross-codec generalization ability.

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 MPEG and JPEG Pleno 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 MPEG and JPEG Pleno 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.

Technical description of the EPFL submission to the JPEG DNA CfP

This document provides a technical description of the codec proposed by EPFL to the JPEG DNA Call for Proposals. The codec we refer to as V-DNA for its versatility, enables the encoding of raw images and already compressed JPEG 1 bitstreams, but the underlying algorithm could be used to encode and transcode any kind of data. The codec is composed of two main modules: the image compression module, handled by the state-of-the-art JPEG XL codec, and the DNA encoding module, implemented using a modified Raptor Code implementation following the RU10 (Raptor Unsystematic) description. The code for encoding and decoding, as well as the objective metrics results, plots and biochemical constraints analysis are available on ISO Documents system with document number WG1M101013-ICQ-EPFL submission to the JPEG DNA CfP.

Sampling color and geometry point clouds from ShapeNet dataset

The popularisation of acquisition devices capable of capturing volumetric information such as LiDAR scans and depth cameras has lead to an increased interest in point clouds as an imaging modality. Due to the high amount of data needed for their representation, efficient compression solutions are needed to enable practical applications. Among the many techniques that have been proposed in the last years, learning-based methods are receiving large attention due to their high performance and potential for improvement. Such algorithms depend on large and diverse training sets to achieve good compression performance. ShapeNet is a large-scale dataset composed of CAD models with texture and constitute and effective option for training such compression methods. This dataset is entirely composed of meshes, which must go through a sampling process in order to obtain point clouds with geometry and texture information. Although many existing software libraries are able to sample geometry from meshes through simple functions, obtaining an output point cloud with geometry and color of the external faces of the mesh models is not a straightforward process for the ShapeNet dataset. The main difficulty associated with this dataset is that its models are often defined with duplicated faces sharing the same vertices, but with different color values. This document describes a script for sampling the meshes from ShapeNet that circumvent this issue by excluding the internal faces of the mesh models prior to the sampling. The script can be accessed from the following link: https://github.com/mmspg/mesh-sampling.