Evaluating color difference measures in images

Ortiz-Jaramillo, B.; Kumcu, Asli; Philips, Wilfried

doi:10.1109/qomex.2016.7498922

Cited by 14 publications

(4 citation statements)

References 30 publications

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“…Although HDR-VDP-2.2 has a lower performance on combined dataset compared to its performance on individual databases, it is among the three most correlated metrics with HDR-VQM and PU-MSSIM on the case excluding Database #2. Interestingly, the HDR-VQM metric, which has been designed to predict video fidelity, gives excellent results also in the case of static images, and (Ortiz-Jaramillo et al 2016), they have lower correlation scores when compared to luminance-only metrics. In fact, this result is not in disagreement with Ortiz-Jaramillo et al (2016), which did not consider compression artifacts in the experiments, as the impact of those on image quality was deemed to be much stronger than color differences.…”

Section: Discussionmentioning

confidence: 99%

An extensive performance evaluation of full-reference HDR image quality metrics

2017

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High dynamic range (HDR) image and video technology has recently attracted a great deal of attention in the multimedia community, as a mean to produce truly realistic video and further improve the quality of experience (QoE) of emerging multimedia services. In this context, measuring the quality of compressed HDR content plays a fundamental role. However, full-reference (FR) HDR visual quality assessment poses new challenges with respect to the conventional low dynamic range case. Quality metrics have to be redesigned or adapted to HDR, and understanding their reliability to predict users' judgments is even more critical due to the still limited availability of HDR displays to perform subjective evaluations. The goal of this paper is to provide a complete and thorough survey of the performance of the most popular HDR FR image quality metrics. To this end, we gather several existing HDR image databases with subjective quality annotations, in addition to a new one created by ourselves. After aligning the scores in these databases, we obtain an extensive set of 690 compressed HDR images, along with their subjective quality. Next, we analyze in depth many FR metrics, including those used in MPEG standardization, using both classical correlation analyses and classification accuracy. We believe that our results could serve as the most complete and comprehensive benchmark of image quality metrics in the field of HDR image compression.

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Section: Discussionmentioning

confidence: 99%

An extensive performance evaluation of full-reference HDR image quality metrics

2017

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“…In order to approximate the subjective score of the color difference due to gamut reduction in an objective manner, we employ the color extension of the structural similarity index (cssim) [49], [50], which can effectively measure perceptually significant structural differences due to gamut reduction between two color images. The preliminary study [1] shows that it performs best with the highest accuracy among eight commonly-used objective color difference metrics [51], [52], [53], [54], [55], [56], [57].…”

Section: Fitting Objective Metricmentioning

confidence: 99%

Wide Color Gamut Image Content Characterization: Method, Evaluation, and Applications

Lee

Vigier

Callet

et al. 2021

IEEE Trans. Multimedia

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In this paper, we propose a novel framework to characterize a wide color gamut image content based on perceived quality due to the processes that change color gamut, and demonstrate two practical use cases where the framework can be applied. We first introduce the main framework and implementation details. Then, we provide analysis for understanding of existing wide color gamut datasets with quantitative characterization criteria on their characteristics, where four criteria, i.e., coverage, total coverage, uniformity, and total uniformity, are proposed. Finally, the framework is applied to content selection in a gamut mapping evaluation scenario in order to enhance reliability and robustness of the evaluation results. As a result, the framework fulfils content characterization for studies where quality of experience of wide color gamut stimuli is involved.Index Terms-Wide color gamut, color gamut mapping, content characterization, content selection, quality of experience.

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“…For instance, Ortiz-Jaramill et al [48] demonstrated that current color difference measures (i.e., FR-IQA methods that compute color differences between processed and reference images) present little correlation with subjective quality scores. Also, even though some DS-IQA methods are able to predict the quality of contrast-distorted images [49], most GP-IQA methods have a poor prediction performance.…”

Section: Introductionmentioning

confidence: 99%

Referenceless image quality assessment by saliency, color-texture energy, and gradient boosting machines

Freitas

Akamine

2018

J Braz Comput Soc

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In most practical multimedia applications, processes are used to manipulate the image content. These processes include compression, transmission, or restoration techniques, which often create distortions that may be visible to human subjects. The design of algorithms that can estimate the visual similarity between a distorted image and its non-distorted version, as perceived by a human viewer, can lead to significant improvements in these processes. Therefore, over the last decades, researchers have been developing quality metrics (i.e., algorithms) that estimate the quality of images in multimedia applications. These metrics can make use of either the full pristine content (full-reference metrics) or only of the distorted image (referenceless metric). This paper introduces a novel referenceless image quality assessment (RIQA) metric, which provides significant improvements when compared to other state-of-the-art methods. The proposed method combines statistics of the opposite color local variance pattern (OC-LVP) descriptor with statistics of the opposite color local salient pattern (OC-LSP) descriptor. Both OC-LVP and OC-LSP descriptors, which are proposed in this paper, are extensions of the opposite color local binary pattern (OC-LBP) operator. Statistics of these operators generate features that are mapped into subjective quality scores using a machine-learning approach. Specifically, to fit a predictive model, features are used as input to a gradient boosting machine (GBM). Results show that the proposed method is robust and accurate, outperforming other state-of-the-art RIQA methods.

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Evaluating color difference measures in images

Cited by 14 publications

References 30 publications

An extensive performance evaluation of full-reference HDR image quality metrics

An extensive performance evaluation of full-reference HDR image quality metrics

Wide Color Gamut Image Content Characterization: Method, Evaluation, and Applications

Referenceless image quality assessment by saliency, color-texture energy, and gradient boosting machines

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