Preparing subjective evaluations for point cloud quality assessment using 2D or 3D planar displays typically requires rendering uncompressed videos, which demands large volumes of data storage and processing, particularly at high resolutions. This paper describes a subjective quality evaluation of point clouds using a head-mounted display (HMD), which allows the direct 3D content depiction and thus eliminates the need for video rendering. The test dataset included six static point clouds encoded with the MPEG codecs V-PCC and G-PCC, the learning-based codec RS-DLPCC, and the Draco codec. The obtained Mean Opinion Scores (MOS) were compared with two other studies using the same dataset but with different displays, notably a 2D display and a 3D stereoscopic display. The reported results indicate that the three studies are highly correlated, with Pearson and Spearman correlation coefficients above 0.9. Moreover, a multi-way ANOVA shows that changing the display does not have a significant effect on the obtained MOS.

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.

Abstract Quality assessment is a key element for the evaluation of hardware and software involved in image and video acquisition, processing, and visualization. In the medical field, user-based quality assessment is still considered more reliable than objective methods, which allow the implementation of automated and more efficient solutions. Regardless of increasing research on this topic in the last decade, defining quality standards for medical content remains a non-trivial task, as the focus should be on the diagnostic value assessed by expert viewers rather than the perceived quality from naïve viewers, and objective quality metrics should aim at estimating the first rather than the latter. In this paper, we present a survey of methodologies used for the objective quality assessment of medical images and videos, dividing them into visual quality-based and task-based approaches. Visual quality-based methods compute a quality index directly from visual attributes, while task-based methods, being increasingly explored, measure the impact of quality impairments on the performance of a specific task. A discussion on the limitations of state-of-the-art research on this topic is also provided, along with future challenges to be addressed.

A study on the quality evaluation of point clouds in the presence of coding distortions is presented. For that, four different point cloud coding solutions, notably the standardized MPEG codecs G-PCC and V-PCC, a deep learning-based coding solution RS-DLPCC, and Draco, are compared using a subjective evaluation methodology. Furthermore, several full-reference, reduced-reference and no-reference point cloud quality metrics are evaluated. Two different point cloud normal computation methods were tested for the metrics that rely on them, notably the Cloud Compare quadric fitting method with radius of five, ten, and twenty and Meshlab KNN with K six, ten, and eighteen. To generalize the results, the objective quality metrics were also benchmarked on a public database, with mean opinion scores available. To evaluate the statistical differences between the metrics, the Krasula method was employed. The Point Cloud Quality Metric reveals the best performance and a very good representation of the subjective results, as well as being the metric with the most statistically significant results. It was also revealed that the Cloud Compare quadric fitting method with radius 10 and 20 produced the most reliable normals for the metrics dependent on them. Finally, the study revealed that the most commonly used metrics fail to accurately predict the compression quality when artifacts generated by deep learning methods are present.

In this paper, the quality evaluation of the responses to the Call for Proposals (CfP) of JPEG Pleno Point Cloud Coding is presented. Three responses to the CfP were evaluated together with the state of the art anchor codecs G-PCC and VPCC from MPEG. The JPEG committee selected a set of eight point clouds that were encoded at different pre-established bitrates. For the subjective evaluation of the responses to the CfP, a set of video sequences were created where the reference and distorted decoded point clouds were rotated about their axes side by side. Furthermore, the objective quality metrics PCQM, PSNR D1, PSNR D2, PSNR Y and PSNR YUV were computed, and compared with the subjective evaluation results. This study revealed that the deep learning solutions outperformed G-PCC but were still below the performance of V-PCC regarding color representation. PCQM showed the best performance in predicting the compression quality.

In this paper, a stability analysis of the JPEG Pleno Learning-based Point Cloud Coding Verification Model (VmUC) is performed. The codec is a deep learning-based solution that is able to compress both color and geometry. Three different training sessions were conducted using the default training set and cost function, and six point clouds were encoded/decoded with the resulting operating points for six target distortion/bitrate ratios. The VmUC performance was compared with the MPEG codecs V-PCC and G-PCC, considering three objective metrics, notably PSNR MSE D1, PSNR MSE D2, and PCQM. PSNR MSE D1 was also computed at each training epoch for the six decoded point clouds. It is concluded that the VmUC is able to outperform G-PCC and V-PCC in geometry encoding. However, it is outperformed by V-PCC in terms of color encoding, namely across all three training sessions. Furthermore, it is also shown that the codec does not present a high level of stability, changing its performance considerably with different training sessions.

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.

In this paper, a subjective evaluation of static point clouds encoded with several codecs is described. Unlike other studies, a stereoscopic 3D display was used to visualize the 3D representation. A set of six point clouds were encoded using a set of state of the art point cloud coding solutions, notably the two MPEG codecs V-PCC and G-PCC, a deep learning solution RS-DLPCC that was the response to a call for evidence on point cloud coding of JPEG Pleno, and the popular DRACO codec. The results of this subjective quality evaluation using a 3D representation visualized in a stereoscopic display were compared with a previous subjective study that used the same content visualized in a 2D display. For that, the results of both tests were compared with the Pearson correlation, Spearman rank order correlation, the root mean square error and the outlier ratio. Moreover, the two subjective evaluation results were statistically analysed to seek for any statistical difference. The two subjective evaluations reveal a very high level of similarity.

Magnetic Resonance Imaging (MRI) is a non-invasive tool for the clinical assessment of low-prevalence neuromuscular disorders. Automated diagnosis methods might reduce the need for biopsies and provide valuable information on disease follow-up. In this paper, three methods are proposed to classify target muscles in Collagen VI-related myopathy cases, based on their degree of involvement, notably a Convolutional Neural Network, a Fully Connected Network to classify texture features, and a hybrid method combining the two feature sets. The proposed methods were evaluated on axial T1-weighted Turbo Spin-Echo MRI from 26 subjects, including Ullrich Congenital Muscular Dystrophy and Bethlem Myopathy patients at different evolution stages. The hybrid model achieved the best cross-validation results, with a global accuracy of 93.8%, and F-scores of 0.99, 0.82, and 0.95, for healthy, mild and moderate/severe cases, respectively.

At its 93rd meeting, JPEG received the responses to the Call for Proposals on JPEG Pleno holography. This activity will lead to the first standardized coding solution for digital holograms. This paper describes the quality evaluation process, existing out of a subjective evaluation study and an objective evaluation based on preselected quality metrics. It also includes a critical evaluation of the challenges encountered when performing quality evaluation of holographic content. Based on the evaluation of the response, the JPEG committee was able to select a very reliable coding solution that depicted a performance well beyond that of the selected anchors.

Recently, a larger interest in the different plenoptic formats, including digital holograms, has emerged. Aside from other challenges that several steps of the holographic pipeline, from digital acquisition to display, have to face, visual quality assessment of compressed holograms is particularly demanding due to the distinct nature of this 3D image modality when compared to regular 2D imaging. There are few studies on holographic data quality assessment, particularly with respect to perceptual effects of lossy compression. This work aims to study the quality evaluation of digital hologram reconstructions, presented on regular 2D displays, in the presence of compression distortions. As there is no established or generally agreed compression methodology for digital holograms compression on the hologram plane with available implementations, a set of state-of-the-art compression codecs, namely HEVC, AV1, and JPEG2000, were used for compression of the digital holograms on the object plane. Both computer generated and optically generated holograms were considered. Two subjective tests were conducted to evaluate distortions caused by compression. The first subjective test was conducted on the reconstructed amplitude images of central views, while the second test was conducted on pseudo-videos generated from the reconstructed amplitudes of different views. The subjective quality assessment was based on mean opinion scores. A selection of objective quality metrics was evaluated, and their correlations with mean opinion scores were computed. The VIFp metrics appeared to have the highest correlation.

In light field image compression, facilitating random access to individual views plays a significant role in decoding views quickly, reducing memory footprint, and decreasing the bandwidth requirement for transmission. Highly efficient light field image compression methods mainly use inter view prediction. Therefore, they typically do not provide random access to individual views. On the other hand, methods that provide full random access usually reduce compression efficiency. To address this trade-off, a light field image encoding method that favors random access is proposed in this paper. Light field image views are grouped into independent (3× 3) views, which are called Macro View Images (MVIs) . To encode MVIs, the central view is used as a reference to compress its adjacent neighboring views using a hierarchical reference structure. To encode the central view of each MVI, the most central view along with the center of a maximum of three MVIs, are used as reference images for the disparity estimation. In addition, the proposed method allows the use of parallel processing to reduce the maximum encoding/decoding time-complexity in multi-core processors. Tile partitioning can also be used to randomly access different regions of the light field images. The simulation results show that the proposed method outperforms other state-of-the-art methods in terms of compression efficiency while providing random access to both views and regions of interest.

Plenoptic representations, like light fields, point clouds or digital holography, provide the means for 3D representations suitable for multiple immersive and computer vision applications. JPEG has been standardizing coding tools for these types of plenoptic data in its project JPEG Pleno. This standardization effort has been developing quality assessment models suitable for the quality evaluation of the coding technologies. In this tutorial the quality assessment methodologies defined for the evaluation of the different proposals of the three plenoptic modalities, are explained. The tutorial also includes possible alternatives considered in the definition of the quality assessment models and the selection of appropriate anchors decided during JPEG Pleno development process.

In this work we compare two different options to perform on-line subjective quality assessment experiments in the context of the Call for Evidence on JPEG Pleno Point Cloud Coding. A deep-learning based point cloud codec submitted to the Call was tested against current MPEG point cloud compression methods. The first option is based on participants downloading the entire set of stimuli and running a set of scripts in MATLAB to perform the experiment. The second option involves the participants accessing a server on the web and viewing and judging the stimuli using a web browser. Quality scores compiled using both methods were compared showing strong correlation. A second analysis compared the quality scores with those obtained in a prior laboratory-based study using higher resolution screens. The entire study also brought to light each option’s unique advantages and disadvantages that make each one better suited to specific types of subjective evaluation contexts and situations.

In preparation of the Call for Proposals on JPEG Pleno Holography, multiple exploration studies are ongoing to define the general procedure for performance assessment of proposed coding solutions. The performance of proposals will be compared against JPEG 2000 and H.265/HEVC intra coding, both serving as anchor codecs. In this paper, we report on the results of a dynamic subjective visual quality assessment procedure. A subset of 8 holograms, selected from the JPEG Pleno Database, is compressed both in hologram and object plane at three different bit rates. Chosen bit rates vary per hologram depending on their content characteristics. Then, for each hologram and its compressed versions, pseudo-video sequences are generated from the reconstructed views along a scan path that involves focus and viewing angle changes and that is hologram specific. A double stimulus simultaneous test, combined with a 5-level impairment-scale scoring protocol is deployed where videos created from the reference and the decoded holograms are visualized side by side on a professional 4K display. Results demonstrate that this test procedure requires deep scenes and sufficient scene complexity throughout the depth stack to allow for adequate stress testing of the codecs under test, particularly those solutions that adhere to compression in the object plane.

Recently, there has been a growing interest in the various formats of plenoptic information, including digital holograms. However, the reduced number of studies on holographic data quality assessment results in a difficult analysis of the effects caused by any type of post-processing procedures, like lossy compression. In this work, several state of the art compression codecs, namely HEVC, AV1, and JPEG2000, are applied to the digital holographic data complex representation on the reconstruction plane. Both computer generated and experimental holograms are considered. The perceptual quality of the amplitude is evaluated to measure the quality degradation at four different bit rates. The performance of a selection of quality metrics is also tested.

This paper presents a quality evaluation study of point cloud codecs that have been recently standardised by the MPEG committee. In particular, a subjective experiment to assess their performance in terms of bitrate against visual quality is designed and realized in four independent laboratories. The experimental setup of each laboratory varies; yet, the obtained subjective scores exhibit high inter laboratory correlation, confirming that the adopted assessment protocol is robust to equipment selection and viewing conditions, ensuring reliability and facilitating repeatability. Our study confirms the superior compression performance of the MPEG V-PCC, when compared to MPEG G-PCC, in the case of static contents. Finally, results from a benchmark of the most popular objective quality metrics using the obtained subjective scores as ground truth, reveal that the point2plane with mean square error is the most accurate quality predictor, closely followed by the point2point also using mean square error as distance measure.

Pseudo-sequence based light field compression methods are a highly efficient solution to compress light field images. They use state-of-the-art video encoders like HEVC to encode the image views. HEVC exploits Coding Tree Unit (CTU) structure which is flexible and highly efficient but it is computationally demanding. Each CTU is examined in various depths, prediction and transformation modes to find an optimal coding structure. Efficiently predicting depth of the coding units can reduce complexity significantly. In this paper, a new depth decision method is introduced which exploits the minimum and maximum of previously encoded co-located coding units in spatially closer reference images. Minimum and maximum depths of these co-located CTUs are computed for each coding unit and are used to limit the depth of the current coding unit. Experimental results show up to 55% and 85% encoding time reduction with serial and parallel processing respectively, at negligible degradations.

In light field compression, besides coding efficiency, providing random access to individual views is also a very significant factor. Highly efficient compression methods usually lack random access. Similarly, random access methods usually reduce the compression efficiency. To address this trade-off, a light field image encoding method is proposed in this paper which favors random access. In the proposed scheme 15×15 view images are divided into 25 independent 3×3 view images which are called Macro View Image (MVI). To encode MVIs, the central view image is used to compress its immediate neighboring view images using a hierarchical reference structure. To encode the central view of each MVI, the most central view image, along with the center of at most three MVIs, are used as the reference images for the disparity estimation. In addition, the proposed method enables the use of parallel computation to improve encoding/decoding time complexity. To reduce memory footprint in case a Region of Interest (ROI) is required, HEVC tile partitioning is used.

In previous work, a digital hologram compression scheme for representation on the object plane was proposed. Compression on object plane for experimental holograms and Computer-Generated Holograms (CGH) proves to be a very efficient model that outperforms the compression on the hologram plane. However, the compression gain is more relevant in CGHs. The difference between experimental holograms and CGHs is related to the fact that CGHs are less affected by speckle noise that is a characteristic of experimental holograms. In the current work, to improve the coding efficiency of the hologram compression scheme is proposed the reduction of speckle noise of experimental holograms. The compression scheme defines a base layer where a 2D version of the object is coded with an image codec standard. The efficiency of this step is much higher in case of CGH when compared to experimental holograms. However, after performing speckle noise reduction before any compression a similar compression efficiency is found. Since the speckle noise reduction is performed only on amplitude data without affecting the phase, is still possible to render 3D features such as depth map, multi-view or to recover holographic interference patterns for further 3D visualization.

Light field imaging is a promising technology for 3D computational photography. As Light Field images are represented for multiple views, their subjective evaluation is a very demanding task. Hence, identifying reliable objective quality assessment methodologies plays a very important role. In this paper six objective quality metrics; PSNR-Y, PSNR-YUV, SSIM-Y, MSSSIM-Y, FSIM-Y and HDRVDP2-Y are assessed for five state-of-the-art codecs at various bit-rates. Moreover, the metrics are computed in the linear, perceptually uniform and perceptual quantizer spaces. The results are compared against those of a subjective study and is concluded that the average FSIM-Y is the most reliable metric. The paper also introduces maps of the objective metrics to evaluate the quality dispersion among the different light field image views.

Recently stakeholders in the area of multimedia representation and transmission have been looking at plenoptic technologies to improve immersive experience. Among these technologies, point clouds denote a volumetric information representation format with important applications in the entertainment, automotive and geographical mapping industries. There is some consensus that state-of-the-art solutions for efficient storage and communication of point clouds are far from satisfactory. This paper describes a study on point cloud quality evaluation, conducted in the context of JPEG Pleno to help define the test conditions of future compression proposals. A heterogeneous set of static point clouds in terms of number of points, geometric structure and represented scenarios were selected and compressed using octree-pruning and a projection-based method, with three different levels of degradation. The models were comprised of both geometrical and color information and were displayed using point sizes large enough to ensure observation of watertight surfaces. The stimuli under assessment were presented to the observers on 2D displays as animations, after defining suitable camera paths to enable visualization of the models in their entirety and realistic consumption. The experiments were carried out in three different laboratories and the subjective scores were used in a series of correlation studies to benchmark objective quality metrics and assess inter-laboratory consistency.

The perceived image quality of a digital hologram of macroscopic objects is affected by its reduced depth of focus and by speckle noise due to coherent illumination. Several filtering techniques have been proposed for speckle noise reduction of digital holograms but there are scarce quality assessment studies regarding phase-shifting digital holograms. Typically, the performance of these filters on experimental holograms is assessed using no reference objective metrics. However, these metrics do not reflect the subjective visual quality perceived by a human observer. In this work, the performance of four speckle reduction algorithms, namely the non-local means, the Lee, the Frost and the block matching 3D filters, using five different parametrizations for each case, are subjectively compared. Due to a large amount of testing data, the subjective test was divided in two: 1) one phase where the effects of the parametrization of each method are evaluated, 2) a second phase where the best parametrization results are compared between them. The results are ranked with respect to the perceived image quality to obtain the Mean Opinion Scores for each filter/parameter combination. As in this case there is no reference image and double stimulus is desirable, the subjective evaluation was achieved using full paired comparison. The experiment indicates that BM3D and Lee are the preferred filters, and reveals a strong dependence of filter performance on hologram characteristics.

Light fields capture a large number of samples of light rays in both intensity and direction terms, which allow post-processing applications such as refocusing, shifting view-point and depth estimation. However, they are represented by huge amount of data and require a high-efficient coding scheme for its compression. In this paper, light field raw image data is decomposed into multi-views and used as a pseudo-sequence input for state-of-the-art codecs such as High Efficiency Video Coding (HEVC). In order to better exploit redundancy between neighboring views and decrease distances between current view and its references instead of using conventional orders, views are divided into four smaller regions and each region is scanned by a snake order. Furthermore, according to this ordering, an appropriate referencing structure is defined that only selects adjacent views as references. Simulation results show that Rate-Distortion performance of proposed method has higher gain than the other state-of-the-art light field compression methods.

Point clouds are one of the most promising technologies for 3D content representation. In this paper, we describe a study on quality assessment of point clouds, degraded by octree-based compression on different levels. The test contents were displayed using Screened Poisson surface reconstruction, without including any textural information, and they were rated by subjects in a passive way, using a 2D image sequence. Subjective evaluations were performed in five independent laboratories in different countries, with the inter-laboratory correlation analysis showing no statistical differences, despite the different equipment employed. Benchmarking results reveal that the state-of-the-art point cloud objective metrics are not able to accurately predict the expected visual quality of such test contents. Moreover, the subjective scores collected from this experiment were found to be poorly correlated with subjective scores obtained from another test involving visualization of raw point clouds. These results suggest the need for further investigations on adequate point cloud representations and objective Quality assessment tools.

Medical professionals are viewing an increasing number of images and videos in their clinical routine. However, various types of distortions can affect medical imaging data, and therefore impact the viewers' experienced quality and their clinical practice. Thus it is necessary to quantify this impact and understand how the viewers, i.e., medical experts, perceive the quality of (distorted) images and videos. In this paper, we present an up-to-date review of the methodologies used in the literature for the subjective quality assessment of medical images and videos and discuss their merits and drawbacks depending on the use case.

In recent years, 3D point clouds have gained more attention with the possibility of applications such as virtual reality, autonomous vehicles and 3D mapping of historical artifacts, among others. However, raw point clouds generate very large amounts of data. Thus, compression is essential to enable emerging 3D systems for communication and storage. This paper presents an inter prediction technique based on the ICP algorithm and variable-size macroblocks that can be used to significantly reduce the number of bits required to represent a point cloud video sequence by inter coding the geometry information. Since consecutive frames in dynamic point cloud sequences are not guaranteed to fill the exact same 3D volume, a spatial alignment step before motion estimation is required to increase the likelihood of good matchings and thus generate a high number of inter-coded macroblocks. A decision step is also included in order to select the most favorable coding mode: intra-coding, inter-coding or inter-coding with macroblock subdivision. The proposed technique was tested in the PCC MPEG reference software and four MPEG test sequences, obtaining average bitrate reductions of about 8% with PSNR gains up to 1dB.

High dynamic range (HDR) imaging has been developed for improved visual representation by capturing a wide range of luminance values. Owing to its properties, HDR content might lead to a larger privacy intrusion, requiring new methods for privacy protection. Previously, false colours were proved to be effective for assuring privacy protection for low dynamic range (LDR) images. In this work, the reliability of false colours when used for privacy protection of HDR images represented by tone‐mapping operators (TMOs) is studied. Two different TMO techniques are tested, a simple TMO based on the Gamma transform and a more complex local TMO. Moreover, two false colour palettes are also tested, and are applied to images that result from both TMOs and also to an LDR image that represents the centre exposure in the image sequence used to create the HDR image. The degree of privacy protection is analysed through both a subjective test using crowdsourcing and an objective test using face recognition algorithms. It is concluded that the application of the two studied false colour palettes reduces the recognition accuracy with respect to both tests.

This paper studies the influence of JPEG-XT on LDR generation using TMOs'. JPEG-XT encodes HDR images into a two layer scheme, encoding a LDR version of the image in a base layer, and the residual HDR information in an enhancement layer. The question addressed here is to understand if this model allows to extract a new LDR representation using a different TMO, independently of the TMO used to generate the LDR image encoded in the base layer. In particular, the use of the Drago, Mai, Mantiuk, Reinhard, and Reinhard & Devlin TMOs' will be considered together with the three main profiles defined on the JPEG-XT standard. Hence, images generate with a TMO applied to the decoded HDR image are compared with the generated from the original HDR image using the popular FSIM metric. Moreover, in cases with similar perceptual quality, the bit rate increase, compared with the JPEG encoding is also studied for the three JPEG-XT profiles.

This paper addresses the need to use the knowledge about the human perceived quality, adding machine learning models to the objective quality estimation. A new technique is proposed based on the division of images into several cells where the mean of the SSIM metric is computed. A sliding window over a grid of cells that divide the image will define a set of image descriptors that are aggregated using a bag of words. This model is able to improve the typical values provided by SSIM and defines a new path for the application of machine learning to image quality evaluation.

The rapid adoption of MPEG-DASH is testament to its core design principles that enable the client to make the informed decision relating to media encoding representations, based on network conditions, device type and preferences. Typically, the focus has mostly been on the different video quality representations rather than audio. However, for device types with small screens, the relative bandwidth budget difference allocated to the two streams may not be that large. This is especially the case if high quality audio is used, and in this scenario, we argue that increased focus should be given to the bit rate representations for audio. Arising from this, we have designed and implemented a subjective experiment to evaluate and analyses the possible effect of using different audio quality levels. In particular, we investigate the possibility of providing reduced audio quality so as to free up bandwidth for video under certain conditions. Thus, the experiment was implemented for live music concert scenarios transmitted over mobile networks, and we suggest that the results will be of significant interest to DASH content creators when considering bandwidth tradeoff between audio and video.

The influence of chromatic impairments on the perceived image quality is studied in this article. Under the D65 standard illuminant, a set of hyperspectral images were represented into the CIELAB color space, and the corresponding chromatic coordinates were subdivided into clusters with the k -means algorithm. Each color cluster was shifted by a predefined chromatic impairment Δ E * ab with random direction in a * b * chromatic coordinates only. Applying impairments of 3, 6, 9, 12, and 15 in a * b * coordinates to five hyperspectral images a set of modified images was generated. Those images were shown to subjects that were asked to rank their quality based on their naturalness. The Mean Opinion Score of the subjective evaluations was computed to quantify the sensitivity to the chromatic variations. This article is also complemented with an objective evaluation of the quality using several state-of-the-art metrics and using the CIEDE2000 color difference among others. Analyzing the correlations between subjective and objective quality evaluation helps us to conclude that the proposed quality estimators based on the CIEDE2000 provide the best representation. Moreover, it was concluded that the established quality metrics only become reliable by averaging their results on each color component.

This paper describes an automatic method for the optic disc localization in retinal images, which is effective and reliable with multiple datasets. Particularly, the described method reveals very effective dealing with retinal images with large pathological signs. The algorithm begins with a new vessel enhancement method based on a modified corner detector. Subsequently, a weighted version of the vessel enhancement is combined with morphological operators, to detect the four main vessels orientations {0(°), 45(°), 90(°), 135(°) }. These four image functions have all the necessary information to determine an initial optic disc localization, resulting in two images that are respectively divided along the vertical or horizontal orientations with different division sizes. Each division is averaged creating a 2-D step function, and a cumulative sum of the different sizes step functions is calculated in the vertical and horizontal orientations, resulting in an initial optic disc position. The final optic disc localization is determined by a vessel convergence algorithm using its two most relevant features; high vasculature convergence and high intensity values. The proposed method was evaluated in eight publicly available datasets, including the STARE and DRIVE datasets. The optic disc was localized correctly in 1752 out of the 1767 retinal images (99.15%) with an average computation time of 18.34 s.

The upcoming JPEG XT is under development for High Dynamic Range (HDR) image compression. This standard encodes a Low Dynamic Range (LDR) version of the HDR image generated by a Tone-Mapping Operator (TMO) using the conventional JPEG coding as a base layer and encodes the extra HDR information in a residual layer. This paper studies the performance of the three profiles of JPEG XT (referred to as profiles A, B and C) using a test set of six HDR images. Four TMO techniques were used for the base layer image generation to assess the influence of the TMOs on the performance of JPEG XT profiles. Then, the HDR images were coded with different quality levels for the base layer and for the residual layer. The performance of each profile was evaluated using Signal to Noise Ratio (SNR), Feature SIMilarity Index (FSIM), Root Mean Square Error (RMSE), and CIEDE2000 color difference objective metrics. The evaluation results demonstrate that profiles A and B lead to similar saturation of quality at the higher bit rates, while profile C exhibits no saturation. Profiles B and C appear to be more dependent on TMOs used for the base layer compared to profile A.

This paper provides a study on the ability of Full-Reference quality metrics to measure the Quality of Experience that results on images that suffer chromatic errors. Considering this, the well known PSNR, MSSIM and VIFP will be compared with the MOS results. It is important to highlight that these measures are usually applied to the image luminance channel. However, in this study only the chromatic components of the image represented in the CIELAB color space have been changed resulting in similar values of luminosity. Moreover, the CIEDE2000 was also considered, because the knowledge of the scene illuminant allows a reliable computation. Using correlation estimates it was concluded that the CIEDE2000 and the MSSIM provides the best MOS estimation. The MSSIM results in a slightly better Pearson correlation value while the CIEDE2000 results in a better RMSE and Outlier Ratio. The VIFP also provides a good correlation, while the PSNR shows a poor correlation with the MOS values.

Iris recognition is a popular technique for recognizing humans. However, as is the case with most biometric traits, it is difficult to collect data that are suitable for use in experiments due to three factors: 1) the substantial amount of data that is required; 2) the time that is spent in the acquisition process; and 3) the security and privacy concerns of potential volunteers. This paper describes a stochastic method for synthesizing ocular data to support experiments on iris recognition. Specifically, synthetic data are intended for use in the most important phases of those experiments: segmentation and signature encoding/matching. The resulting data have an important characteristic: they simulate image acquisition under uncontrolled conditions. We have experimentally confirmed that the proposed strategy can mimic the data degradation factors that usually result from such conditions. Finally, we announce the availability of an online platform for generating degraded synthetic ocular data. This platform is freely accessible worldwide.

A study on the perceived quality of images displayed with color changes is presented. Under the D65 standard illuminant colors are changed in the CIE 1976 (L*a*b*) color space, with the application of a predefined chromatic error ΔE*ab. The colors were initially divided into clusters with the K-Means algorithm. Each color cluster is shifted by the predefined chromatic error with a random direction in a*b* chromatic coordinates. Applying the ΔE*ab errors of 3, 6, 9, 12 and 15 units to the five hyperspectral images a set of modified images was collected. Those images were shown to individuals, that were asked to rank those images quality based on their naturalness. The Medium Opinion Scores was computed and allowed to test and quantify the sensibility to color changes.

Edges are one of the most important image visual features. They are highly related with shapes and can also be representative of the image textures. Edge orientations histograms are usually very reliable descriptors suitable for image analysis, search and retrieval. In this work edges detected with Canny algorithm are described by their angular orientations. The resulting descriptor is resilient to image rotation and image translation. It is also resilient to noise. An example of automatic image semantic annotation using this description method is reported using a database with 738 images. The K Nearest Neighbor is used as classifier and the Manhattan distance is used for image similarity computation. The annotation that results with this description method is compared with the provided with other well known descriptors. These examples show that a reliable high level automatic description based in the semantic content can be extracted.

This paper describes a method of approximating a shape contour with a polygon. The polygon vertices are extracted from the curvature extremes, through a scale-space description of the contour, via linear diffusion. These vertices are located on the contour points where the sharper changes of the contour directions occur. Using a proper strategy, a set of extremes that result in a given approximation level is chosen. By adding new vertices, the approximation level can be improved, and a scalable representation of the contour is identified. This method results in an approximation that discriminates local from global geometric features and provides a good visual representation of the original contour. This polygonal approximation method is used for scalable encoding of the shape contours. In this regard, an encoding technique suitable for scalable polygonal approximation has been developed. We show that encoding the approximated polygons result in a good relation between the distortion and the bitrate. Finally, we show that in addition to coding this method can be efficiently used for shape comparison and shape retrieval.

A non-linear diffusion filtering method of an image with local adjustment of the diffusivity function K factor, is presented.

The use of the curvature extremes of the smoothed contour for a reliable shape comparison suitable for shape retrieving is analysed. The shape contours are smoothed using linear diffusion of the contour. A diffusion scale proportional to the contour dimension is chosen, improving the uniform scaling invariance. The relative positions of the smoothed contours curvature extremes are compared, resulting in a shape similarity measure. This method is compared with the resulting from comparing the maxima of the scale-space maps of the contours' curvature zero-crossing (CSS), used in MPEG-7. It is shown that those maxima always coincide with a curvature extreme at the maximum scale. Retrieving examples using the proposed method, are compared with the results of a CSS implementation. A visual improvement of the retrieving results was found. The results were also compared with a polygonal approximation based method.

The non-linear diffusion of the method of P. Perona and J. Malik (see IEEE Trans. Pattern Anal. and Machine Intelligence, vol.PAMI-12, no.7, p.629-39, 1990; Proc. IEEE Comput. Soc. Workshop on Comput. Vision, p.16-22, 1987) is applied to a contour. Unlike most contour diffusion techniques, the contour is described by the angle variation, and the non-linear diffusion procedure is applied to the contour turning angle. The Perona and Malik model determines how strongly diffusion acts on the original function, and depends on a factor K, estimated automatically. In areas with spatial concentration of strong changes of angle, this factor is also adjusted to reduce the contour small perturbations and noise effects.

A method of improving the traditional polygonal approximation methods is described. It has properties such as scalability, resilience to noise, controlled levels of approximation and good visual representation. These properties are a direct result of the use of scale space information to derive the vertices positions of the polygonal approximation. The vertices are situated on the contour points where the changes of the contour directions are at their major sharp positions. An encoding method with a scalable polygonal approximation of shapes is defined, which results in a good relation between the distortion and the bit rate.

The emerging MPEG-7 standard demands shape description and shape retrieval techniques. Polygonal approximations of the shape contours give attractive solutions in this domain, because of the description simplicity. This paper introduces a shape matching technique based on the turning function comparison of the shape contour polygonal approximations.

New MPEG-4 functionalities require the segmentation of input video into different layers (VOPs). Usually, these layers contain arbitrarily shaped objects representing meaningful content of the video stream. With the introduction of the new functionalities in MPEG4, the need of objective and subjective assessment of segmented image quality has emerged. In this paper we introduce an efficient and reliable metric to evaluate segmentation results by comparing them with a given ground truth. Beyond this application the proposed technique can be used for real-time shape description and retrieval in the context of the emerging MPEG7, as well as in general pattern recognition tasks. Selected results obtained by using this metric within these application areas are reported.