International audienceThere are several standard methods for evaluating the performance of models for objective quality assessment with respect to results of subjective tests. However, all of them suffer from one or more of the following drawbacks: They do not consider the uncertainty in the subjective scores, requiring the models to make certain decision where the correct behavior is not known. They are vulnerable to the quality range of the stimuli in the experiments. In order to compare the models, they require a mapping of predicted values to the subjective scores, thus not comparing the models exactly as they are used in the real scenarios. In this paper, new methodology for objective models performance evaluation is proposed. The method is based on determining the classification abilities of the models considering two scenarios inspired by the real applications. It does not suffer from the previously stated drawbacks and enables to easily evaluate the performance on the data from multiple subjective experiments. Moreover, techniques to determine statistical significance of the performance differences are suggested. The proposed framework is tested on several selected metrics and datasets, showing the ability to provide a complementary information about the models' behavior while being in parallel with other state-of-the-art methods

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Abstract-The procedures commonly used to evaluate the performance of objective quality metrics rely on ground truth mean opinion scores and associated confidence intervals, which are usually obtained via direct scaling methods. However, indirect scaling methods, such as the paired comparison method, can also be used to collect ground truth preference scores. Indirect scaling methods have a higher discriminatory power and are gaining popularity, for example in crowdsourcing evaluations. In this paper, we present how the classification errors, an existing analysis tool, can also be used with subjective preference scores. Additionally, we propose a new analysis tool based on the receiver operating characteristic analysis. This tool can be used to further assess the performance of objective metrics based on ground truth preference scores. We provide a MATLAB script with an implementation of the proposed tools and we show one example of application of the proposed tools.

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Most of the effort in image quality assessment (QA) has been so far dedicated to the degradation of the image. However, there are also many algorithms in the image processing chain that can enhance the quality of an input image. These include procedures for contrast enhancement, deblurring, sharpening, up-sampling, denoising, transfer function compensation, etc. In this work, possible strategies for the quality assessment of sharpened images are investigated. This task is not trivial because the sharpening techniques can increase the perceived quality, as well as introduce artifacts leading to the quality drop (over-sharpening). Here, the framework specifically adapted for the quality assessment of sharpened images and objective metrics comparison in this context is introduced. However, the framework can be adopted in other quality assessment areas as well. The problem of selecting the correct procedure for subjective evaluation was addressed and a subjective test on blurred, sharpened, and over-sharpened images was performed in order to demonstrate the use of the framework. The obtained ground-truth data were used for testing the suitability of state-ofthe- art objective quality metrics for the assessment of sharpened images. The comparison was performed by novel procedure using ROC analyses which is found more appropriate for the task than standard methods. Furthermore, seven possible augmentations of the no-reference S3 metric adapted for sharpened images are proposed. The performance of the metric is significantly improved and also superior over the rest of the tested quality criteria with respect to the subjective data.

Objective video quality metrics are designed to estimate the quality of experience of the end user. However, these objective metrics are usually validated with video streams degraded under common distortion types. In the presented work, we analyze the performance of published and known full-reference and noreference quality metrics in estimating the perceived quality of adaptive bit-rate video streams knowingly out of scope. Experimental results indicate not surprisingly that state of the art objective quality metrics overlook the perceived degradations in the adaptive video streams and perform poorly in estimating the subjective quality results.

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International audienceThe main obstacle preventing High Dynamic Range (HDR) imaging from becoming standard in image and video processing industry is the challenge of displaying the content. The prices of HDR screens are still too high for ordinary customers. During last decade, a lot of effort has been dedicated to finding ways to compress the dynamic range for legacy displays with simultaneous preservation of details in highlights and shadows which cannot be achieved by standard systems. These dynamic range compression techniques are called tone-mapping operators (TMO) and introduce novel distortions such as spatially non-linear distortion of contrast or naturalness corruption. This paper provides an analysis of objective no-reference naturalness, contrast and colorfulness measures in the context of tone-mapped images evaluation. Reliable measures of these features could be further merged together into single overall quality metric. The main goal of the paper is to provide an initial study of the problem and identify the potential candidates for such a combinatio