Subjective and Objective Quality Assessments of Display Products

Zhang, Huiqing; Li, Donghao; Yu, Yibing; Guo, Nan

doi:10.3390/e23070814

Cited by 6 publications

(4 citation statements)

References 42 publications

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“…In most practical problems, the quality the work of methods of image segmentation is considered as a measure of the similarity of the image segmented by the algorithm and the reference algorithm segmented by the expert. Thus, the quality assessment image segmentation can be determined both at the objective (quantitative) levels and at the subjective (qualitative) [11,12].…”

Section: mentioning

confidence: 99%

The Choice of Quality Indicator for the Image Segmentation Evaluation

Khudov¹,

Makoveichuk²,

Khizhnyak³

et al. 2022

IJETAE

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In this paper, the main attention is focused on the stage of image segmentation and on the choice of a quality indicator for the evaluation of image segmentation. In general form the segmentation problem of color and tone images is formulated in. The main approaches that are used in methods and techniques of image segmentation are highlighted. The need to evaluate the results of the work of methods and techniques of image segmentation to assess their performance has been established. The options for the image segmentation quality assessing of both at the objective (quantitative) level and at the subjective (qualitative) level are considered. The main features of qualitative segmentation are highlighted. Two main groups of indicators for evaluating the quality of segmentation were analyzed: indicators based on comparison with the reference result of image segmentation and the indicators for which benchmark segmentation is not required. Their main disadvantages when used in evaluating the results of image segmentation are described. The information quality indicator – Kullback-Leibner divergence for the image segmentation quality assessing of the results of image segmentation is considered. The result of image segmentation by the Canny edge detector is presented. The estimation of the quality of the segmented image was calculated using the information Kullback-Leibner divergence for pairs of images of different scales. The paper shows show a graph of the dependence of the value of the Kullback-Leibler divergence on the value of the scale factor of the original image. Keywords— Canny edge detector, image segmentation, objective criteria, Kullback-Leibler divergence, quality indicator.

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Section: mentioning

confidence: 99%

The Choice of Quality Indicator for the Image Segmentation Evaluation

Khudov¹,

Makoveichuk²,

Khizhnyak³

et al. 2022

IJETAE

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show abstract

“…In the past decade, an increasing number of publicly available IQA databases containing natural, screen-content, or synthetic images have been released for research [21]. The images found in IQA databases have been evaluated in subjective user studies involving human subjects in a laboratory environment [11] or crowdsourcing experiment [10] to obtain individual quality ratings. These IQA databases can be divided into two groups with respect to the type of image distortions (artificial or authentic).…”

Section: Databasesmentioning

confidence: 99%

“…Single stimulation, double stimulation, and stimulation comparison methods are the most common ones for subjective scoring in the literature. For more details about subjective scoring, we refer to the work of Zhang et al [11]. An overview about a wide range of publicly available IQA databases can be found in [12].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Benford’s Law for No-Reference Quality Assessment of Natural, Screen-Content, and Synthetic Images

Varga

2021

Electronics

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With the tremendous growth and usage of digital images, no-reference image quality assessment is becoming increasingly important. This paper presents in-depth analysis of Benford’s law inspired first digit distribution feature vectors for no-reference quality assessment of natural, screen-content, and synthetic images in various viewpoints. Benford’s law makes a prediction for the probability distribution of first digits in natural datasets. It has been applied among others for detecting fraudulent income tax returns, detecting scientific fraud, election forensics, and image forensics. In particular, our analysis is based on first digit distributions in multiple domains (wavelet coefficients, DCT coefficients, singular values, etc.) as feature vectors and the extracted features are mapped onto image quality scores. Extensive experiments have been carried out on seven large image quality benchmark databases. It has been demonstrated that first digit distributions are quality-aware features, and it is possible to reach or outperform the state-of-the-art with them.

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“…Stereo video can provide a more immersive watching experience for viewers, so stereo video systems have been widely used in various fields [ 1 , 2 ]. The processing in stereo video system will inevitably introduce distortions in stereo videos, which will seriously affect the user experience [ 3 ]. Therefore, the research on quality metric is of great significance for performance optimization of stereo video system and has aroused more and more attention [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Stereo Video Quality Metric Based on Multi-Dimensional Analysis

Luo

et al. 2021

Entropy

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Stereo video has been widely applied in various video systems in recent years. Therefore, objective stereo video quality metric (SVQM) is highly necessary for improving the watching experience. However, due to the high dimensional data in stereo video, existing metrics have some defects in accuracy and robustness. Based on the characteristics of stereo video, this paper considers the coexistence and interaction of multi-dimensional information in stereo video and proposes an SVQM based on multi-dimensional analysis (MDA-SVQM). Specifically, a temporal-view joint decomposition (TVJD) model is established by analyzing and comparing correlation in different dimensions and adaptively decomposes stereo group of frames (sGoF) into different subbands. Then, according to the generation mechanism and physical meaning of each subband, histogram-based and LOID-based features are extracted for high and low frequency subband, respectively, and sGoF quality is obtained by regression. Finally, the weight of each sGoF is calculated by spatial-temporal energy weighting (STEW) model, and final stereo video quality is obtained by weighted summation of all sGoF qualities. Experiments on two stereo video databases demonstrate that TVJD and STEW adopted in MDA-SVQM are convincible, and the overall performance of MDA-SVQM is better than several existing SVQMs.

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Subjective and Objective Quality Assessments of Display Products

Cited by 6 publications

References 42 publications

The Choice of Quality Indicator for the Image Segmentation Evaluation

The Choice of Quality Indicator for the Image Segmentation Evaluation

Analysis of Benford’s Law for No-Reference Quality Assessment of Natural, Screen-Content, and Synthetic Images

Stereo Video Quality Metric Based on Multi-Dimensional Analysis

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