Future Directions in Image Quality

2019 ◽  
Vol 2019 (1) ◽  
pp. 399-403
Author(s):  
Seyed Ali Amirshahi ◽  
Marius Pedersen
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Short Paper ◽  
Future Directions ◽  
Perceptual Judgement ◽  
Made In

With the advancements made in the field of image processing and computer vision, the last few decades have seen an increase in studies focused on image quality assessment. While this has resulted in the introduction of different new metrics which some show high correlation with the perceptual judgement of the human observers there still exists a huge room for improvement. In this short paper which is prepared as a complement to the workshop on Future Directions in Image Quality at CIC 27 in Paris, France we aim to introduce future directions in the field and challenges facing ahead.

scholarly journals Deep Learning in Image Quality Assessment: Past, Present, and What Lies Ahead

2021 ◽  
Vol 2021 (1) ◽  
pp. 1-4
Author(s):  
Seyed Ali Amirshahi
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Machine Learning Techniques ◽  
Short Paper ◽  
Subjective Quality Assessment ◽  
Learning Techniques ◽  
Objective Quality

Quality assessment of images plays an important role in different applications in image processing and computer vision. While subjective quality assessment of images is the most accurate approach due to issues objective quality metrics have been the go to approach. Until recently most such metrics have taken advantage of different handcrafted features. Similar (but with a slower speed) to other applications in image processing and computer vision, different machine learning techniques, more specifically Convolutional Neural Networks (CNNs) have been introduced in different tasks related to image quality assessment. In this short paper which is a supplement to a focal talk given with the same title at the London Imaging Meeting (LIM) 2021 we aim to provide a short timeline on how CNNs have been used in the field of image quality assessment so far, how the field could take advantage of CNNs to evaluate the image quality, and what we expect will happen in the near future.

A WAVELET-BASED IMAGE QUALITY ASSESSMENT METHOD

2008 ◽  
Vol 06 (04) ◽  
pp. 541-551 ◽  
Author(s):  
WEN LU ◽  
XINBO GAO ◽  
DACHENG TAO ◽  
XUELONG LI
Keyword(s):  
Image Processing ◽  
Computational Complexity ◽  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Assessment Method ◽  
Reference Image ◽  
Subjective Perception ◽  
Good Accordance ◽  
Quality Assessment Method

Image quality is a key characteristic in image processing,10,11 image retrieval,12,13 and biometrics.14 In this paper, a novel reduced-reference image quality assessment method is proposed based on wavelet transform. By simulating the human visual system, we take the variance of the visual sensitive coefficients into account to measure a distorted image. The computational complexity of the proposed method is much lower compared with some existing methods. Experimental results demonstrate its advantages in terms of correlation coefficient, outlier ratio, transmitted information, and CPU cost. Moreover, it is also illustrated that the proposed method has a good accordance with human subjective perception.

A Novel Method of Image Quality Assessment

2014 ◽  
Vol 556-562 ◽  
pp. 5064-5067 ◽  
Author(s):  
Ming Wei Guo ◽  
Chen Bin Zhang ◽  
Zong Hai Chen
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Selective Attention ◽  
Visual System ◽  
Quality Assessment ◽  
Human Visual System ◽  
Image Quality Assessment ◽  
Visual Selective Attention ◽  
Research Areas ◽  
Novel Method

Image quality assessment (IQA) is one of the hot research areas in the field of image processing. For the reason that human being is the final receiver of the image, the image quality assessment should match the characteristics of human visual system. In this paper, we propose a novel method of image quality assessment which uses the visual selective attention of human visual system. For an image of a certain category, our method firstly detects the object in it and then calculate the saliency of the object. Lastly we use the combination of the detector’s score and the saliency as the image quality assessment. Experiments on some images of Pascal VOC dataset and INRIA dataset show that our method does well in image quality assessment.

scholarly journals On the Application LBP Texture Descriptors and Its Variants for No-Reference Image Quality Assessment

2018 ◽  
Vol 4 (10) ◽  
pp. 114 ◽  
Author(s):  
Pedro Garcia Freitas ◽  
Luísa da Eira ◽  
Samuel Santos ◽  
Mylene Farias
Keyword(s):  
Computer Vision ◽  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Local Binary Patterns ◽  
Reference Image ◽  
Fundamental Interest ◽  
Wide Range ◽  
Estimate Quality

Automatic assessing the quality of an image is a critical problem for a wide range of applications in the fields of computer vision and image processing. For example, many computer vision applications, such as biometric identification, content retrieval, and object recognition, rely on input images with a specific range of quality. Therefore, an effort has been made to develop image quality assessment (IQA) methods that are able to automatically estimate quality. Among the possible IQA approaches, No-Reference IQA (NR-IQA) methods are of fundamental interest, since they can be used in most real-time multimedia applications. NR-IQA are capable of assessing the quality of an image without using the reference (or pristine) image. In this paper, we investigate the use of texture descriptors in the design of NR-IQA methods. The premise is that visible impairments alter the statistics of texture descriptors, making it possible to estimate quality. To investigate if this premise is valid, we analyze the use of a set of state-of-the-art Local Binary Patterns (LBP) texture descriptors in IQA methods. Particularly, we present a comprehensive review with a detailed description of the considered methods. Additionally, we propose a framework for using texture descriptors in NR-IQA methods. Our experimental results indicate that, although not all texture descriptors are suitable for NR-IQA, many can be used with this purpose achieving a good accuracy performance with the advantage of a low computational complexity.

Image Quality Assessment Using Computer Vision

2019 ◽  
Vol 2019 (10) ◽  
pp. 317-1-317-5
Author(s):  
Zhi Li ◽  
Palghat Ramesh ◽  
Chu-Heng Liu
Keyword(s):  
Computer Vision ◽  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment

scholarly journals Seven Challenges in Image Quality Assessment: Past, Present, and Future Research

2013 ◽  
Vol 2013 ◽  
pp. 1-53 ◽  
Author(s):  
Damon M. Chandler
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Natural Images ◽  
Interactive Effects ◽  
Future Research ◽  
Full Reference ◽  
Remarkable Progress ◽  
Intense Research ◽  
Made In

Image quality assessment (IQA) has been a topic of intense research over the last several decades. With each year comes an increasing number of new IQA algorithms, extensions of existing IQA algorithms, and applications of IQA to other disciplines. In this article, I first provide an up-to-date review of research in IQA, and then I highlight several open challenges in this field. The first half of this article provides discuss key properties of visual perception, image quality databases, existing full-reference, no-reference, and reduced-reference IQA algorithms. Yet, despite the remarkable progress that has been made in IQA, many fundamental challenges remain largely unsolved. The second half of this article highlights some of these challenges. I specifically discuss challenges related to lack of complete perceptual models for: natural images, compound and suprathreshold distortions, and multiple distortions, and the interactive effects of these distortions on the images. I also discuss challenges related to IQA of images containing nontraditional, and I discuss challenges related to the computational efficiency. The goal of this article is not only to help practitioners and researchers keep abreast of the recent advances in IQA, but to also raise awareness of the key limitations of current IQA knowledge.

scholarly journals Computer Vision in Precipitation Nowcasting: Applying Image Quality Assessment Metrics for Training Deep Neural Networks

Atmosphere ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 244 ◽  
Author(s):  
Quang-Khai Tran ◽  
Sa-kwang Song
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Mean Squared Error ◽  
Neural Network Models ◽  
Common Mean ◽  
Radar Images ◽  
Squared Error

This paper presents a viewpoint from computer vision to the radar echo extrapolation task in the precipitation nowcasting domain. Inspired by the success of some convolutional recurrent neural network models in this domain, including convolutional LSTM, convolutional GRU and trajectory GRU, we designed a new sequence-to-sequence neural network structure to leverage these models in a realistic data context. In this design, we decreased the numbers of channels in high abstract recurrent layers rather than increasing them. We formulated the task as a problem of encoding five radar images and predicting 10 steps ahead at the pixel level, and found that using only the common mean squared error can misguide the training and mislead the testing. Especially, the image quality of last predictions usually degraded rapidly. As a solution, we employed some visual image quality assessment techniques including Structural Similarity (SSIM) and multi-scale SSIM to train our models. Experimental results show that our structure was more tolerant to increasing uncertainty in the data, and the use of image quality metrics can significantly reduce the blurry image issue. Moreover, we found that using SSIM was very effective and a combination of SSIM with mean squared error and mean absolute error yielded the best prediction quality.

scholarly journals REMOTE SENSING IMAGE QUALITY ASSESSMENT EXPERIMENT WITH POST-PROCESSING

2018 ◽  
Vol XLII-3 ◽  
pp. 665-668
Author(s):  
W. Jiang ◽  
S. Chen ◽  
X. Wang ◽  
Q. Huang ◽  
H. Shi ◽  
...  
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Quality Assessment ◽  
Physical Simulation ◽  
Image Quality Assessment ◽  
Imaging System ◽  
Post Processing ◽  
Improve Image Quality ◽  
System Parameters ◽  
Imaging Parameters

This paper briefly describes the post-processing influence assessment experiment, the experiment includes three steps: the physical simulation, image processing, and image quality assessment. The physical simulation models sampled imaging system in laboratory, the imaging system parameters are tested, the digital image serving as image processing input are produced by this imaging system with the same imaging system parameters. The gathered optical sampled images with the tested imaging parameters are processed by 3 digital image processes, including calibration pre-processing, lossy compression with different compression ratio and image post-processing with different core. Image quality assessment method used is just noticeable difference (JND) subject assessment based on ISO20462, through subject assessment of the gathered and processing images, the influence of different imaging parameters and post-processing to image quality can be found. The six JND subject assessment experimental data can be validated each other. Main conclusions include: image post-processing can improve image quality; image post-processing can improve image quality even with lossy compression, image quality with higher compression ratio improves less than lower ratio; with our image post-processing method, image quality is better, when camera MTF being within a small range.

scholarly journals A narrative move towards the exploration of gauging of image quality

2019 ◽  
Vol 3 (3) ◽  
Author(s):  
Tamil Kodi ◽  
Siva Prasad ◽  
Venkateswara Kiran ◽  
Praveen Kumar
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
The One ◽  
The Individual

Image quality assessment (IQA) acting as a noteworthy part in a variety of image processing applications. Manipulative eminence of an image is essential predicament in image and record handling and a range of procedure have been anticipated for IQA.widespread psychological substantiation shows that humans favor to conduct evaluations qualitatively comparative than numerical. However most frequently used IQA metrics are not reliable fine with the individual judgments of image quality. For the majority of the applications, the perceptual momentous compute is the one which can routinely estimate the worth of images or videos involving reliable behavior. This article explains about the various methods and their behavior towards the assessment of image quality.

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