Enhanced image no‐reference quality assessment based on colour space distribution

Lv, Hao; Li, Ce; Zhang, Dong; Zhou, Yannan; Du, Shaoyi

doi:10.1049/iet-ipr.2019.0856

Cited by 9 publications

(2 citation statements)

References 35 publications

(99 reference statements)

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“…In literature, many quality aware features both in spatial domain and transformed domain have been considered. Some of the efficient features are based on information from discrete cosine transform (DCT) statistics [11], natural scene statistics (NSS) [12], hybrid NSS [13], free energy principle and HVS inspired features [14], color space distribution [15] and a lot more. The NR-IQA algorithms that do not consider any additional information are able to quantify image quality when images are affected by some distortions, but not for all.…”

Section: Introductionmentioning

confidence: 99%

Goal oriented image quality assessment

Kiruthika

Masilamani

2021

IET Image Processing

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The area of image quality assessment(IQA) is an active research area in image processing and computer vision. All IQA algorithms reported in literature are attempting to quantify only the visual quality of the images/videos. An interesting question to be answered is that given a goal (task) and an image (with good visual quality), is the image good to achieve the goal by the best possible algorithm. In an attempt to spur the research community to answer this question, a new paradigm of IQA, called goal oriented IQA (GO-IQA), is introduced. GO-IQA is defined as given an image and a goal, predicting how good the image is to achieve that goal by the best possible algorithm. The need for GO-IQA is that if GO-IQA score is less, then any arbitrary algorithm attempting to achieve the goal will eventually fail. In this paper, considering segmentation as goal, a GO-IQA algorithm is proposed to predict how good the image is to do accurate segmentation by the best possible segmentation algorithm. A support vector regression model has been trained with features related to image segmentation along with the accuracies of a class of well-known image segmentation algorithms. In the process, we have obtained encouraging results in terms of the Pearson linear correlation coefficient and mean squared error. The predictions given by the proposed model have been validated with the scores of state of the art segmentation algorithm.

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Section: Introductionmentioning

confidence: 99%

Goal oriented image quality assessment

Kiruthika

Masilamani

2021

IET Image Processing

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“…However, in most practical applications, the original signal is not available at the client or receiver end. Using FR approach the metrics such as Structural Similarity Image Index (SSIM) [4], Multi-Scale Structural Similarity Index (MS-SSIM) [5], Feature Similarity Index (FSIM) [6], Gradient Magnitude Similarity Deviation (GMSD) [7], and Perceptual Similarity Index (PSIM) [8] have been proposed. • Reduced reference (RR) In RR methods, it is not necessary to have access of the original multimedia contents for quality assessment purpose.…”

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confidence: 99%

Reduced reference image and video quality assessments: review of methods

Dost

Saud

Shabbir

et al. 2022

J Image Video Proc.

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With the growing demand for image and video-based applications, the requirements of consistent quality assessment metrics of image and video have increased. Different approaches have been proposed in the literature to estimate the perceptual quality of images and videos. These approaches can be divided into three main categories; full reference (FR), reduced reference (RR) and no-reference (NR). In RR methods, instead of providing the original image or video as a reference, we need to provide certain features (i.e., texture, edges, etc.) of the original image or video for quality assessment. During the last decade, RR-based quality assessment has been a popular research area for a variety of applications such as social media, online games, and video streaming. In this paper, we present review and classification of the latest research work on RR-based image and video quality assessment. We have also summarized different databases used in the field of 2D and 3D image and video quality assessment. This paper would be helpful for specialists and researchers to stay well-informed about recent progress of RR-based image and video quality assessment. The review and classification presented in this paper will also be useful to gain understanding of multimedia quality assessment and state-of-the-art approaches used for the analysis. In addition, it will help the reader select appropriate quality assessment methods and parameters for their respective applications.

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