2016
DOI: 10.1007/s11760-016-1009-z
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Image quality assessment based on regions of interest

Abstract: International audienceMost methods in the literature of image quality assessment (IQA) use whole image information for measuring image quality. However, human perception does not always use this criterion to assess the quality of images. Individuals usually provide their opinions by considering only some parts of an image, called regions of interest. Based on this hypothesis, in this research work, a segmentation technique is initially employed to obtain a bi-level image map composed of the foreground and back… Show more

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Cited by 17 publications
(8 citation statements)
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“…The mean opinion score (MOS) of each image is computed by averaging the individual ratings across subjects, and used as ground truth quality score. The MOS values are in the [1,100] range.…”
Section: Resultsmentioning
confidence: 99%
See 2 more Smart Citations
“…The mean opinion score (MOS) of each image is computed by averaging the individual ratings across subjects, and used as ground truth quality score. The MOS values are in the [1,100] range.…”
Section: Resultsmentioning
confidence: 99%
“…DIIVINE [34] 0.90 0.88 BRISQUE [31] 0.93 0.91 BLIINDS-II [39] 0.93 0.91 Low Level Features [21] 0.94 0.94 Multi-task CNN [20] 0.93 0.94 HOSA [49] 0.95 0.93 DeepBIQ 0.97 0.96 [31] 0.93 0.91 BLIINDS-II [39] 0.92 0.90 MGMSD [1] 0.88 0.89 Low Level Features [21] 0.89 0.88 Multi-task CNN [20] 0.90 0.91 Shallow CNN [19] 0.90 0.92 DeepBIQ 0.95 0.95 Table 9 Median LCC and median SROCC across 100 trainval-test random splits of the TID2013.…”
Section: Methods Lcc Sroccmentioning
confidence: 99%
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“…As the text in a natural scene or video image is the main source of semantic information and provides rich information about the content of the image, there are several real‐world text mining applications, such as contextual advertising, business intelligence, and content enrichment. It has also been shown in the literature that foreground information, including text and salient objects, draw the attention of viewers (Alaei et al, 2015, 2017; Judd et al, 2009). Moreover, text detection followed by recognition is an essential part of several computer vision applications, such as automatic sign reading, language translation, autonomous car driving, and multimedia retrieval.…”
Section: Introductionmentioning
confidence: 99%
“…Given an image, IQA systems are designed to automatically estimate the quality score. Existing IQA methods can be classified into three major categories: full-reference image quality assessment (FR-IQA) algorithms, e.g., [ 1 , 2 , 3 , 4 , 5 , 6 , 7 ], reduced-reference image quality assessment (RR-IQA) algorithms, e.g., [ 8 , 9 , 10 , 11 ], and no-reference/blind image quality assessment (NR-IQA) algorithms, e.g., [ 12 , 13 , 14 , 15 , 16 ]. FR-IQA methods compare the distorted image with respect to the reference image in order to predict a quality score, and because of that, they requires both the original image alongside the corrupted one.…”
Section: Introductionmentioning
confidence: 99%