DNA has been proposed as an alternative support for data storage due to its lower energy consumption and longer lifespan when compared to conventional techniques. DNA-based storage faces several challenges, particularly in managing errors that arise during synthesis and sequencing. The JPEG Committee has been working towards a DNA-based image coding standard and has proposed an initial prototype without including error-correction mechanisms. Inserted in the context of the JPEG DNA standardization, this paper presents two joint error-correcting pipelines for DNA-based image storage combining cyclic redundancy checks, Reed-Solomon codes, convolutional codes, and Raptor codes. The proposed pipelines are evaluated across various channel models and benchmarked against state-of-the-art, demonstrating enhanced coding performance, reduced complexity, and improved flexibility. The efficacy of the proposed systems has resulted in their inclusion in the JPEG DNA verification model. The results presented in this paper can serve as a reference to benchmark against future ideas on joint source-channel coding of images in DNA.

High dynamic range (HDR) and wide color gamut (WCG) technologies significantly improve color reproduction compared to standard dynamic range (SDR) and standard color gamuts, resulting in more accurate, richer, and more immersive images. However, HDR increases data demands, posing challenges for bandwidth efficiency and compression techniques. Advances in compression and display technologies require more precise image quality assessment, particularly in the high-fidelity range where perceptual differences are subtle. To address this gap, we introduce AIC-HDR2025, the first such HDR dataset, comprising 100 test images generated from five HDR sources, each compressed using four codecs at five compression levels. It covers the high-fidelity range, from visible distortions to compression levels below the visually lossless threshold. A subjective study was conducted using the JPEG AIC-3 test methodology, combining plain and boosted triplet comparisons. In total, 34,560 ratings were collected from 151 participants across four fully controlled labs. The results confirm that AIC-3 enables precise HDR quality estimation, with 95% confidence intervals averaging a width of 0.27 at 1 JND. In addition, several recently proposed objective metrics were evaluated based on their correlation with subjective ratings. The dataset is publicly available1.

Point clouds allow for the representation of 3D multimedia content as a set of disconnected points in space. Their inherent irregular geometric nature poses a challenge to efficient compression, a critical operation for both storage and transmission. This paper proposes a VAE-inspired codec tailored for dynamic point cloud geometry compression, taking advantage of a temporal autoregressive hyperprior to enhance compression performance. Specifically, features derived from adjacent point cloud frames help build a hyperprior for conditional entropy coding. Sparse convolutions are leveraged to reach higher computational efficiency when compared to 3D dense convolutions. Remarkably, the proposed approach achieves an average 60.2% BD-rate gain against the contemporary V-PCC compression standard from MPEG.

As applications using immersive media gained increased 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 standards mentioned above is based on a different algorithm, introducing distinct types of degradation that may impair the quality of experience when lossy compression is applied. Although the impact on perceptual quality can be accurately evaluated during subjective quality assessment experiments, objective quality metrics also predict 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.

The past decades have witnessed rapid growth in imaging as a major form of communication between individuals. Due to recent advances in capture, storage, delivery and display technologies, consumers demand improved perceptual quality while requiring reduced storage. In this context, research and innovation in lossy image compression have steered towards methods capable of achieving high compression ratios without compromising the perceived visual quality of images, and in some cases even enhancing the latter. Subjective visual quality assessment of images plays a fundamental role in defining quality as perceived by human observers. Although the field of image compression is constantly evolving towards efficient solutions for higher visual qualities, standardized subjective visual quality assessment protocols are still limited to those proposed in ITU-R Recommendation BT.500 and JPEG AIC standards. The number of comprehensive and in-depth studies where different protocols are compared is still insufficient. Moreover, previous works have not investigated the effectiveness of these methods on higher quality ranges, using recent image compression methods. In this paper, subjective visual scores collected from three subjective image quality assessment protocols, namely the Double Stimulus Continuous Quality Scale (DSCQS) and two test methods described in the JPEG AIC Part 2 standard, are compared between different laboratories under similar controlled conditions. The analysis of the experimental results has revealed that the DSCQS protocol is highly influenced by the quality of the reference images and experience of the subjects, while the JPEG AIC Part 2 specifications produce more stable results but are expensive and only suitable for a limited range of qualities. These emphasize the need for new robust subjective image quality assessment methodologies able to discriminate in the range of qualities generally demanded by consumers, i.e. from high to nearly visually lossless.

Point clouds are effective data structures for the representation of three-dimensional media and hence adopted in a wide range of practical applications. In many cases, the portrayed data is expected to be visualized by humans. After acquisition, point clouds may undergo different processing operations such as compression or denoising, potentially affecting their perceived quality. Although subjective experiments are still the most reliable form of assessing the intensity of degradation, they are expensive and time-consuming, pushing many systems to depend on objective metrics. Such algorithms are used to model the human visual system, and their performance is usually assessed through their correlation with subjective visual quality scores. In this paper, an objective quality metric capable of evaluating distortions between a reference and a distorted point cloud at multiple scales is presented. The proposed metric is based on the point cloud structural similarity metric (PointSSIM), which computes a score based on the difference between statistical estimators obtained on the distribution of the luminance attribute over local neighborhoods. A collection of PointSSIM scores is produced for multiple scales obtained through the voxelization of both models at different bit depth precisions. These scores are then pooled through a weighted sum, with the importance of each scale being defined through logistic fitting to subjective mean opinion scores, producing one MS-PointSSIM score. Three datasets were employed for fitting and performance assessment, demonstrating a clear advantage of the proposed metric when compared to the single-scale baseline. Moreover, the presented MS-PointSSIM is shown to be the best predictor according to the average Pearson correlation coefficient across the three datasets when compared to state-of-the-art metrics.

Visual data play a crucial role in modern society, and the rate at which images and videos are acquired, stored, and exchanged every day is rapidly increasing. Image compression is the key technology that enables storing and sharing of visual content in an efficient and cost-effective manner, by removing redundant and irrelevant information. On the other hand, image compression often introduces undesirable artifacts that reduce the perceived quality of the media. Subjective image quality assessment experiments allow for the collection of information on the visual quality of the media as perceived by human observers, and therefore quantifying the impact of such distortions. Nevertheless, the most commonly used subjective image quality assessment methodologies were designed to evaluate compressed images with visible distortions, and therefore are not accurate and reliable when evaluating images having higher visual qualities. In this paper, we present a dataset of compressed images with quality levels that range from high to nearly visually lossless, with associated quality scores in JND units. The images were subjectively evaluated by expert human observers, and the results were used to define the range from high to nearly visually lossless quality. The dataset is made publicly available to researchers, providing a valuable resource for the development of novel subjective quality assessment methodologies or compression methods that are more effective in this quality range.

The growing adoption of point clouds as an imaging modality has stimulated the search for efficient solutions for compression. Learning-based algorithms have been reporting increasingly better performance and are drawing the attention from the research community and standardisation groups such as JPEG and MPEG. Learned autoencoder architectures based on 3D convolutional layers are popular solutions and have demonstrated higher performance when adopting latent space entropy modeling based on learned hyperpriors. We propose an enhanced entropy model that takes into account both the hyperprior and previously encoded latent features to estimate the mean and scale of compressed features. The obtained results show a large increase in performance, with a BD PSNR gain of 5.75dB when compared to the Octree coding module in G-PCC for the D2 PSNR metric. We also perform an ablation study to quantify the impact of network parameters in the performance of the model, drawing useful insights for future research.

Immersive imaging modalities have been receiving growing attention over the last years. In this context, point clouds demonstrated to be a competitive data representation format, mainly due to its compatibility with most acquisition devices such as LiDAR scans and depth cameras. On the other hand, the large associated data volume is a challenge to storage and transmission, resulting in the need for efficient point cloud compression methods. Multiple subjective studies have been conducted to assess the performance of such compression methods, mostly limiting the analysis to flat monitors or virtual and augmented reality headsets. In this paper, we investigate the impact of a novel eye-sensing light field display on several aspects of quality of experience, as well as on the subjective perception of compression artifacts. The two visualization devices have been observed to create distinct user experiences which lead to noticeable differences in subjective opinion. The advantages and disadvantages of each visualization strategy are underlined, based on rigorous statistical analysis. The subjectively annotated dataset is also released in order to foster future research.

Point clouds are among popular visual representations for immersive media. However, the vast amount of information generated during their acquisition requires effective compression for practical applications. Although relevant activities from standardization bodies have led to state-of-the-art compression using conventional methods, learning-based encoders have recently emerged as promising solutions with comparable performance while offering additional attractive features. Yet, there is still a large unexplored space for research that can lead to further advances. In this paper, we propose a block prediction module for bit-rate reduction of geometry-only point clouds. Our method exploits spatial redundancies at the decoding stage between block partitions in the point cloud, and predicts a query block using Generative Adversarial Networks. Results show performance improvements of the objective metrics at low bit-rates, after integration in a baseline auto-encoder architecture.

Point cloud is a promising imaging modality for the representation of 3D media. The vast volume of data associated with it requires efficient compression solutions, with lossy algorithms leading to larger bit-rate savings at the expense of visual impairments. While conventional encoding approaches rely on efficient data structures, recent methods have incorporated deep learning for rate-distortion optimization, while inducing perceptual degradations of different natures. To measure the magnitude of such distortions, subjective or objective quality evaluation methodologies are employed. Lately, a remarkable amount of efforts has been devoted to the development of point cloud objective quality metrics, which have been reported to attain high prediction accuracy. However, their performance and generalization capabilities haven’t been evaluated yet in presence of artifacts from learning-based codecs. In this study, we tackle this matter by conducting the first crowdsourcing experiment for point cloud quality reported in the literature, in order to obtain subjective ratings for point cloud models whose topology and color attributes are encoded by both conventional and data-driven methods. Using the subjective scores as ground truth, the performance of a large pool of state-of-the-art quality metrics is rigorously benchmarked, drawing useful insights regarding their efficacy.