Are existing procedures enough? Image and video quality assessment: review of subjective and objective metrics

Ouni, Sonia; Chambah, Majed; Herbin, Michel; Zagrouba, Ezzeddine

doi:10.1117/12.760803

Cited by 17 publications

(13 citation statements)

References 41 publications

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“…x and y represent 2D kernel's width and height in pixels. i varies from 1 to 3 for Luminance plane (Q 1 ) and from 1 to 2 for red-green (Q 2 ) and blue-yellow (Q 3 ) as shown in Table 1. More details of the spatial filtering can be found in [28,29,30,31,32].…”

Section: The Grayscale Indexmentioning

confidence: 99%

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Quality Assessment from Grayscale to Color Images

Hassan¹,

Bashraheel²

2017

wjcat

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Color images reveal more meaningful information to the human observers rather than grayscale ones. Regardless of the advantages of the existing well-known objective image quality metrics, one of the common and major limitations of these metrics is that they evaluate the quality of grayscale images only and don't make use of color information. In this paper we propose an improved method for image quality assessment that adds a color comparison to the criteria of the well-known Structural Similarity index (SSIM). We validated the proposed metric on the TID2013 image database and the experimental results show that the quality scores given by the proposed metric are more consistent with HVS than other state-of-art metrics.

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Section: The Grayscale Indexmentioning

confidence: 99%

“…On the other hand the objective image quality assessment methods are computer based methods that can automatically predict the perceived image quality. Hence the objective image quality assessment methods gained more popularity although they do not necessarily correlate well with the quality as perceived by humans [1,2].…”

Section: Introductionmentioning

confidence: 99%

Quality Assessment from Grayscale to Color Images

Hassan¹,

Bashraheel²

2017

wjcat

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“…An RR metric defines what information has to be extracted form the original image, so it can be compared with the one extracted in the distorted version. In the literature, the most used FR image quality assessments are error-based methods [15]. Thus, these measures are performed by pixel based difference metrics like Delta E ( E), MSE, PSNR, etc.…”

Section: Introductionmentioning

confidence: 99%

SCID: full reference spatial color image quality metric

et al. 2009

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The most used full reference image quality assessments are error-based methods. Thus, these measures are performed by pixel based difference metrics like Delta E ( E), MSE, PSNR, etc. Therefore, a local fidelity of the color is defined. However, these metrics does not correlate well with the perceived image quality. Indeed, they omit the properties of the HVS. Thus, they cannot be a reliable predictor of the perceived visual quality. All this metrics compute the differences pixel to pixel. Therefore, a local fidelity of the color is defined. However, the human visual system is rather sensitive to a global quality. In this paper, we present a novel full reference color metric that is based on characteristics of the human visual system by considering the notion of adjacency. This metric called SCID for Spatial Color Image Difference, is more perceptually correlated than other color differences such as Delta E. The suggested full reference metric is generic and independent of image distortion type. It can be used in different application such as: compression, restoration, etc.

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“…Objective evaluation is automated, hence it costs less than a subjective evaluation, plus it can be done in real-time since it needs no user interaction. Objective quality metrics can be full reference, reduced reference or reference free [21] Moreover, automatic objective assessment systems do not necessarily correlate well with perceived quality [20,21]. Ideally, a quality assessment system would perceive and measure image or video impairments just like a human being.…”

mentioning

confidence: 99%

“…Subjective approaches, which to date are the only widely recognized method of determining actual perceived quality, are complex and time-consuming, both in their preparation and execution [21]. Subjective evaluation is formalized with defined procedures [20]. Objective quality evaluation use metrics to evaluate image quality.…”

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confidence: 99%

Toward an automatic subjective image quality assessment system

et al. 2009

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Usually in the field of image quality assessment the terms "automatic" and "subjective" are often incompatible. In fact, when it comes to image quality assessment, we have mostly two kinds of evaluation techniques: subjective evaluation and objective evaluation. Only objective evaluation techniques being automatizable, while subjective evaluation techniques are performed by a series of visual assessment done by expert or non-expert observers. In this paper, we will present a first attempt to an automatic subjective quality assessment system. The system computes some perception correlated color metrics from a learning set of images. During the learning stage a subjective assessment by users is required so that the system matches the subjective opinions with computed metrics on a variety of images. Once the learning process is over, the system operates in an automatic mode using only the learned knowledge and the reference free computed metrics from the images to assess. Results and also future prospects of this work are presented. CONTEXTWhen it comes to image quality assessment, we have mostly two kinds of evaluation techniques [20] : subjective evaluation and objective evaluation. These evaluations can be done with a priori information (with reference) or without use of a priori information (reference free). Subjective approaches, which to date are the only widely recognized method of determining actual perceived quality, are complex and time-consuming, both in their preparation and execution [21]. Subjective evaluation is formalized with defined procedures [20]. Objective quality evaluation use metrics to evaluate image quality. Objective evaluation is automated, hence it costs less than a subjective evaluation, plus it can be done in real-time since it needs no user interaction. Objective quality metrics can be full reference, reduced reference or reference free [21] Moreover, automatic objective assessment systems do not necessarily correlate well with perceived quality [20,21]. Ideally, a quality assessment system would perceive and measure image or video impairments just like a human being. As long as quality metrics are not correlated with human perception, subjective evaluation is still mandatory. This explains why in some domains such as photography and old film restoration, where there is no reference to compare to, subjective quality evaluation is the most reliable technique used. In the last works we presented some original reference free metrics [22][23][24][25] that correlated well with human perception (metrics for contrast and color quality based on statistical and perceptual approaches). These perception correlated metrics were a first step to bridge the gap between subjective and automatic approaches. In fact, since perceptual correlated metrics are available, automatic computation and learning -and hence automatic assessment-become possible.In this paper, we will present a first attempt to an automatic subjective quality assessment system. The system computes some perception correlated color me...

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Are existing procedures enough? Image and video quality assessment: review of subjective and objective metrics

Cited by 17 publications

References 41 publications

Quality Assessment from Grayscale to Color Images

Quality Assessment from Grayscale to Color Images

SCID: full reference spatial color image quality metric

Toward an automatic subjective image quality assessment system

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