Deep Convolutional Neural Models for Picture-Quality Prediction: Challenges and Solutions to Data-Driven Image Quality Assessment

2017 ◽  
Vol 34 (6) ◽  
pp. 130-141 ◽  
Author(s):  
Jongyoo Kim ◽  
Hui Zeng ◽  
Deepti Ghadiyaram ◽  
Sanghoon Lee ◽  
Lei Zhang ◽  
...  
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Data Driven ◽  
Quality Prediction ◽  
Picture Quality ◽  
Neural Models

scholarly journals Deep Quality: A Deep No-reference Quality Assessment System

2016 ◽  
Vol 2 (1) ◽  
Author(s):  
Prajna Paramita Dash ◽  
Akshaya Mishra ◽  
Alexander Wong
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Assessment System ◽  
Quality Prediction ◽  
Video Capture ◽  
Multi Scale ◽  
The Past ◽  
Full Reference ◽  
Reference Quality

Image quality assessment (IQA) continues to garner great interestin the research community, particularly given the tremendousrise in consumer video capture and streaming. Despite significantresearch effort in IQA in the past few decades, the area of noreferenceimage quality assessment remains a great challenge andis largely unsolved. In this paper, we propose a novel no-referenceimage quality assessment system called Deep Quality, which leveragesthe power of deep learning to model the complex relationshipbetween visual content and the perceived quality. Deep Qualityconsists of a novel multi-scale deep convolutional neural network,trained to learn to assess image quality based on training samplesconsisting of different distortions and degradations such as blur,Gaussian noise, and compression artifacts. Preliminary results usingthe CSIQ benchmark image quality dataset showed that DeepQuality was able to achieve strong quality prediction performance(89% patch-level and 98% image-level prediction accuracy), beingable to achieve similar performance as full-reference IQA methods.

scholarly journals Cross-Domain Feature Similarity Guided Blind Image Quality Assessment

2022 ◽  
Vol 15 ◽  
Author(s):  
Chenxi Feng ◽  
Long Ye ◽  
Qin Zhang
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Perceptual Quality ◽  
Synthetic Image ◽  
Quality Prediction ◽  
Cross Domain ◽  
Blind Image Quality Assessment ◽  
Feature Similarity ◽  
Blind Image

This work proposes an end-to-end cross-domain feature similarity guided deep neural network for perceptual quality assessment. Our proposed blind image quality assessment approach is based on the observation that features similarity across different domains (e.g., Semantic Recognition and Quality Prediction) is well correlated with the subjective quality annotations. Such phenomenon is validated by thoroughly analyze the intrinsic interaction between an object recognition task and a quality prediction task in terms of characteristics of the human visual system. Based on the observation, we designed an explicable and self-contained cross-domain feature similarity guided BIQA framework. Experimental results on both authentical and synthetic image quality databases demonstrate the superiority of our approach, as compared to the state-of-the-art models.

A Novel Hybrid Image Quality Assessment Algorithm

2011 ◽  
Vol 4 (4) ◽  
pp. 107-108
Author(s):  
Deepa Maria Thomas ◽  
◽  
S. John Livingston
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment

Document Image Quality Assessment with Relaying Reference to Determine Minimum Readable Resolution for Compression

2020 ◽  
Vol 2020 (9) ◽  
pp. 323-1-323-8
Author(s):  
Litao Hu ◽  
Zhenhua Hu ◽  
Peter Bauer ◽  
Todd J. Harris ◽  
Jan P. Allebach
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Research Area ◽  
Input Image ◽  
Quality Score ◽  
Document Image ◽  
Digital Cameras ◽  
Active Research ◽  
Traditional Approaches

Image quality assessment has been a very active research area in the field of image processing, and there have been numerous methods proposed. However, most of the existing methods focus on digital images that only or mainly contain pictures or photos taken by digital cameras. Traditional approaches evaluate an input image as a whole and try to estimate a quality score for the image, in order to give viewers an idea of how “good” the image looks. In this paper, we mainly focus on the quality evaluation of contents of symbols like texts, bar-codes, QR-codes, lines, and hand-writings in target images. Estimating a quality score for this kind of information can be based on whether or not it is readable by a human, or recognizable by a decoder. Moreover, we mainly study the viewing quality of the scanned document of a printed image. For this purpose, we propose a novel image quality assessment algorithm that is able to determine the readability of a scanned document or regions in a scanned document. Experimental results on some testing images demonstrate the effectiveness of our method.

No-Reference Image Quality Assessment Based on Multi-Order Gradients Statistics

2020 ◽  
Vol 64 (1) ◽  
pp. 10505-1-10505-16
Author(s):  
Yin Zhang ◽  
Xuehan Bai ◽  
Junhua Yan ◽  
Yongqi Xiao ◽  
C. R. Chatwin ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Assessment Method ◽  
Image Distortion ◽  
Image Feature ◽  
Reference Image ◽  
Gradient Magnitude ◽  
Distortion Type

Abstract A new blind image quality assessment method called No-Reference Image Quality Assessment Based on Multi-Order Gradients Statistics is proposed, which is aimed at solving the problem that the existing no-reference image quality assessment methods cannot determine the type of image distortion and that the quality evaluation has poor robustness for different types of distortion. In this article, an 18-dimensional image feature vector is constructed from gradient magnitude features, relative gradient orientation features, and relative gradient magnitude features over two scales and three orders on the basis of the relationship between multi-order gradient statistics and the type and degree of image distortion. The feature matrix and distortion types of known distorted images are used to train an AdaBoost_BP neural network to determine the image distortion type; the feature matrix and subjective scores of known distorted images are used to train an AdaBoost_BP neural network to determine the image distortion degree. A series of comparative experiments were carried out using Laboratory of Image and Video Engineering (LIVE), LIVE Multiply Distorted Image Quality, Tampere Image, and Optics Remote Sensing Image databases. Experimental results show that the proposed method has high distortion type judgment accuracy and that the quality score shows good subjective consistency and robustness for all types of distortion. The performance of the proposed method is not constricted to a particular database, and the proposed method has high operational efficiency.

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