Statistical evaluation of image quality measures

Avcibaş, Ismail; Sankur, B.; Sayood, Khalid

doi:10.1117/1.1455011

Cited by 530 publications

(42 citation statements)

References 44 publications

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“…Subsequently, the participant had to view each of the seven decoded HDRVs including the hidden reference and perform a qualitative assessment as to how much the decoded HDRVs resembled the ground truth HDRV in the center. Based on their judgement, the participants positioned the corresponding thumbnails to one of the blank positions on the right labeled [1][2][3][4][5][6][7] where 1 being an HDRV with least distortion compared to the reference and 7 being the HDRV with most visible distortions. …”

Section: Methodsmentioning

confidence: 99%

“…Avcıbaş et al [1] and Sheikh et al [41] evaluated a number of QA metrics on distorted still images and concluded that metrics based on spectral magnitude error, perception, absolute norm and edge stability are most suitable for detecting image artefacts. They also conclude that although multiple QA metrics perform well on multiple image datasets none of them performed at par with subjective quality assessment.…”

Section: Related Workmentioning

confidence: 99%

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Objective and subjective evaluation of High Dynamic Range video compression

Mukherjee

Debattista

Bashford-Rogers

et al. 2016

Signal Processing: Image Communication

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Copyright and reuse:The Warwick Research Archive Portal (WRAP) makes this work by researchers of the University of Warwick available open access under the following conditions. Copyright © and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners. To the extent reasonable and practicable the material made available in WRAP has been checked for eligibility before being made available.Copies of full items can be used for personal research or study, educational, or not-for-profit purposes without prior permission or charge. Provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way. A note on versions:The version presented here may differ from the published version or, version of record, if you wish to cite this item you are advised to consult the publisher's version. Please see the 'permanent WRAP URL' above for details on accessing the published version and note that access may require a subscription. AbstractA number of High Dynamic Range (HDR) video compression algorithms proposed to date have either been developed in isolation or only-partially compared with each other. Previous evaluations were conducted using quality assessment error metrics, which for the most part were developed for qualitative assessment of Low Dynamic Range (LDR) videos. This paper presents a comprehensive objective and subjective evaluation conducted with six published HDR video compression algorithms. The objective evaluation was undertaken on a large set of 39 HDR video sequences using seven numerical error metrics namely: PSNR, logPSNR, puPSNR, puSSIM, Weber MSE, HDR-VDP and HDR-VQM. The subjective evaluation involved six short-listed sequences and two ranking-based subjective experiments with hidden reference at two different output bitrates with 32 participants each, who were tasked to rank distorted HDR video footage compared to an uncompressed version of the same footage. Results suggest a strong correlation between the objective and subjective evaluation. Also, non-backward compatible compression algorithms appear to perform better at lower output bit rates than backward compatible algorithms across the settings used in this evaluation.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Objective and subjective evaluation of High Dynamic Range video compression

Mukherjee

Debattista

Bashford-Rogers

et al. 2016

Signal Processing: Image Communication

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“…It is more difficult to perform a mathematical comparison of their performances. Thus, to adequately evaluate the quality of such metrics statistical experiments are needed [8] [9]. To this purpose, a large database of distorted test images is usually prepared, and the Mean Opinion Score (MOS) from a large number of human observers is collected.…”

Section: Introductionmentioning

confidence: 99%

Color image database for evaluation of image quality metrics

Ponomarenko

Егиазарян

Astola

et al. 2008

2008 IEEE 10th Workshop on Multimedia Signal Processing

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In this contribution, a new image database for testing full-reference image quality assessment metrics is presented. It is based on 1700 test images (25 reference images, 17 types of distortions for each reference image, 4 levels for each type of distortion). Using this image database, 654 observers from three different countries (Finland, Italy, and Ukraine) have carried out about 400000 individual human quality judgments (more than 200 judgments for each distorted image). The obtained mean opinion scores for the considered images can be used for evaluating the performances of visual quality metrics as well as for comparison and for the design of new metrics. The database, with testing results, is freely available.

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“…As multiple observers can assign different fidelity scores to an image based on their subjective judgments, the observers' responses to the image have been typically pooled into a single value to provide an overall indication of the image fidelity. [1][2][3][4][5][6][8][9][10][11] However, such pooling obscures the interobserver variability, which may be important in interpreting multiple observers' response patterns.…”

Section: Introductionmentioning

confidence: 99%