Encoding Visual Sensitivity by MaxPol Convolution Filters for Image Sharpness Assessment

Hosseini, Mahdi S.; Zhang, Yueyang; Plataniotis, Konstantinos N.

doi:10.1109/tip.2019.2906582

Cited by 54 publications

(51 citation statements)

References 48 publications

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“…In order to better verify the obvious superiority of our proposed NQMDP model, we compared NQMDP with ten state-of-the-art relevant IQA models, which include five classes: (1) evaluation methods for NSIs including NFERM [5] and HOSA [14]; (2) evaluation methods for image sharpness including ARISMC [43] and HVS [44]; (3) evaluation methods for image contrast including NIQMC [8] and BIQME [6]; (4) evaluation methods without opinion scores including NIQE [13] and SNP-NIQE [15]; (5) evaluation methods for SCIs including BQMS [18] and ASIQE [19]. In order to fairly compare the performance of all the above algorithms, we used the five metrics recommended by the video quality expert group [45].…”

Section: Experiments and Discussionmentioning

confidence: 99%

“…Specifically, the scatter plot of the NQMDP model is more slender than the scatter plot of the NIQE model (second place). ( 2 [5], HOSA [14], ARISMC [43], HVS [44], NIQMC [8], BIQME [6], NIQE [13], SNP−NIQE [15], and our NQMDP on DPQAD database.…”

Section: Experiments and Discussionmentioning

confidence: 99%

“…Figure 7. Scatter plots of MOS versus NFERM[5], HOSA[14], ARISMC[43], HVS[44], NIQMC[8], BIQME[6], NIQE[13], SNP−NIQE[15], and our NQMDP on DPQAD database.…”

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confidence: 99%

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Subjective and Objective Quality Assessments of Display Products

Zhang

et al. 2021

Entropy

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In recent years, people’s daily lives have become inseparable from a variety of electronic devices, especially mobile phones, which have undoubtedly become necessity in people’s daily lives. In this paper, we are looking for a reliable way to acquire visual quality of the display product so that we can improve the user’s experience with the display product. This paper proposes two major contributions: the first one is the establishment of a new subjective assessment database (DPQAD) of display products’ screen images. Specifically, we invited 57 inexperienced observers to rate 150 screen images showing the display product. At the same time, in order to improve the reliability of screen display quality score, we combined the single stimulation method with the stimulation comparison method to evaluate the newly created display products’ screen images database effectively. The second one is the development of a new no-reference image quality assessment (IQA) metric. For a given image of the display product, first our method extracts 27 features by analyzing the contrast, sharpness, brightness, etc., and then uses the regression module to obtain the visual quality score. Comprehensive experiments show that our method can evaluate natural scene images and screen content images at the same time. Moreover, compared with ten state-of-the-art IQA methods, our method shows obvious superiority on DPQAD.

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Section: Experiments and Discussionmentioning

confidence: 99%

Section: Experiments and Discussionmentioning

confidence: 99%

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Subjective and Objective Quality Assessments of Display Products

Zhang

et al. 2021

Entropy

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“…A specific type of NR-IQA methods of high relevance are NR image sharpness assessment (NR-ISA) methods that are designed specifically for evaluating image blur or sharpness. Most of these methods are based on domain knowledge, including human visual system (HVS) [13], [14], Fourier phase [15], complex wavelet domain phase coherence [16], local variation [17], and sparse image representation [18] models.…”

Section: Introductionmentioning

confidence: 99%

Real Versus Fake 4k - Authentic Resolution Assessment

Shah

Akundy

Wang

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In recent years, the native 4K/Ultra High Definition (UHD) resolution has been trending towards the new normal of video content creation and distribution, but the practical pipelines of video acquisition, production and delivery often involve downscaling stages where the spatial resolution drops below the 4K level. Even though the video may be upscaled back to 4K/UHD resolution later, the content has lost its authentic resolution. This work aims at authentic resolution assessment (ARA). We first construct a database of over 10,000 real and fake 4K/UHD images. We then develop a two-stage ARA (TSARA) approach that classifies a video frame to have real or fake 4K resolution, where the first stage classifies local patches using a convolutional neural network (CNN), and the second stage aggregates local assessment into a global image level decision using logistical regression. Experimental results show that the proposed approach achieves high accuracy at low computational cost, and outperforms state-of-the-art no-reference (NR) image quality assessment (IQA) and image sharpness assessment (ISA) models. The built database and the proposed method are made publicly available 1 .

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“…Finally, a percentage fusion strategy was utilized to predict the overall sharpness score. Hosseini et al designed an HVS-related filter (HVS-MaxPol) to extract blur-sensitive features [11], the high-order center moment after image filtering was calculated as the sharpness score. This method utilizes HVS characteristics more effectively and achieves high accuracy on both synthetically and real blurred image databases.…”

Section: Introductionmentioning

confidence: 99%

No-reference Image Blur Assessment Based on Multi-scale Spatial Local Features

Sun¹,

Cui²,

Gan³

et al. 2020

KSII TIIS

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Blur is an important type of image distortion. How to evaluate the quality of blurred image accurately and efficiently is a research hotspot in the field of image processing in recent years. Inspired by the multi-scale perceptual characteristics of the human visual system (HVS), this paper presents a no-reference image blur/sharpness assessment method based on multi-scale local features in the spatial domain. First, considering various content has different sensitivity to blur distortion, the image is divided into smooth, edge, and texture regions in blocks. Then, the Gaussian scale space of the image is constructed, and the categorized contrast features between the original image and the Gaussian scale space images are calculated to express the blur degree of different image contents. To simulate the impact of viewing distance on blur distortion, the distribution characteristics of local maximum gradient of multi-resolution images were also calculated in the spatial domain. Finally, the image blur assessment model is obtained by fusing all features and learning the mapping from features to quality scores by support vector regression (SVR). Performance of the proposed method is evaluated on four synthetically blurred databases and one real blurred database. The experimental results demonstrate that our method can produce quality scores more consistent with subjective evaluations than other methods, especially for real burred images.

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Encoding Visual Sensitivity by MaxPol Convolution Filters for Image Sharpness Assessment

Cited by 54 publications

References 48 publications

Subjective and Objective Quality Assessments of Display Products

Subjective and Objective Quality Assessments of Display Products

Real Versus Fake 4k - Authentic Resolution Assessment

No-reference Image Blur Assessment Based on Multi-scale Spatial Local Features

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