Segmentation-based Perceptual Image Quality Assessment (SPIQA)

Ghanem, Bernard; Resendiz, Esther; Ahuja, Narendra

doi:10.1109/icip.2008.4711774

Cited by 8 publications

(2 citation statements)

References 8 publications

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“…In addition to the methods mentioned in the previous sections, numerous other techniques have been employed in IQA algorithms. For example, IQA algorithms have been developed to assess quality based on different color spaces [206], based on image segmentation and/or region-based analysis [207][208][209] and based on the use of additional features [210][211][212][213][214][215][216][217].…”

Section: Methods Based On Othermentioning

confidence: 99%

Seven Challenges in Image Quality Assessment: Past, Present, and Future Research

Chandler

2013

ISRN Signal Processing

375

191

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Image quality assessment (IQA) has been a topic of intense research over the last several decades. With each year comes an increasing number of new IQA algorithms, extensions of existing IQA algorithms, and applications of IQA to other disciplines. In this article, I first provide an up-to-date review of research in IQA, and then I highlight several open challenges in this field. The first half of this article provides discuss key properties of visual perception, image quality databases, existing full-reference, no-reference, and reduced-reference IQA algorithms. Yet, despite the remarkable progress that has been made in IQA, many fundamental challenges remain largely unsolved. The second half of this article highlights some of these challenges. I specifically discuss challenges related to lack of complete perceptual models for: natural images, compound and suprathreshold distortions, and multiple distortions, and the interactive effects of these distortions on the images. I also discuss challenges related to IQA of images containing nontraditional, and I discuss challenges related to the computational efficiency. The goal of this article is not only to help practitioners and researchers keep abreast of the recent advances in IQA, but to also raise awareness of the key limitations of current IQA knowledge.

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Section: Methods Based On Othermentioning

confidence: 99%

Seven Challenges in Image Quality Assessment: Past, Present, and Future Research

Chandler

2013

ISRN Signal Processing

375

191

View full text Add to dashboard Cite

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“…Therefore, estimating image quality for images captured by such devices is of high demand. As a result, many automatic methods have been proposed in the literature [1,6,9,10,13,16,[27][28][29] to deal with objective image quality assessment (IQA). Based on availability of reference images for computing image quality, the existing methods have been categorized into three main groups called: a) Full Reference (FR), b) No Reference (NR), and c) Reduced Reference (RR) image quality assessment.…”

Section: Introductionmentioning

confidence: 99%

Image quality assessment based on regions of interest

Alaei

Raveaux

Conte

2016

SIViP

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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 background information. A patch selection strategy is then proposed to choose some particular patches based on the foreground information as the regions of interest for IQA. Three recent IQA methods in the literature are considered to demonstrate the improvement in IQA when using only the extracted regions of interest. To evaluate the impact of the proposed patch selection strategy in various IQA metrics, three publicly available datasets were used for experiments. Experimental results have revealed that our proposal, based on the regions of interest, can improve quality measures of three IQA methods

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Sub-block Features Based Image Retrieval

Sajwan

Goyal

2014

Computational Intelligence in Data Mining - Volume 1

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In various domains like security, education, biomedicine etc., the volume of digital data is increasing rapidly, and this is becoming a challenge to retrieve the information from the storage media. Content-based image retrieval systems (CBIR) aim at retrieving from large image databases the images similar to the given query image based on the similarity between image features. This paper aim to discuss and solve the problem of designing sub-block features based image retrieval. Firstly, this paper outlines a description of the primitive features of an image. Then, the proposed methodology for partitioning the image and extracting its colour and texture is described. The algorithms used to calculate the similarity between extracted features, are then explained. Finally, we compared with some other existing CBIR methods, using the WANG database, which is widely used for CBIR performance evaluation, and the results demonstrate the proposed approach outperforms other existing methods considered.

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Segmentation-based Perceptual Image Quality Assessment (SPIQA)

Cited by 8 publications

References 8 publications

Seven Challenges in Image Quality Assessment: Past, Present, and Future Research

Seven Challenges in Image Quality Assessment: Past, Present, and Future Research

Image quality assessment based on regions of interest

Sub-block Features Based Image Retrieval

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