Objective Quality Assessment of Interpolated Natural Images

Yeganeh, Hojatollah; Rostami, Mohammad; Wang, Zhou

doi:10.1109/tip.2015.2456638

Cited by 43 publications

(32 citation statements)

References 35 publications

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“…To the best of our knowledge, this is the largest evaluation study carried out in IQA literature. In addition to FR and NR IQA that are surveyed and evaluated in this paper, there are other types of IQA problems such as reduced-reference (RR) IQA [1], [2], and IQA of reference/test images across different spatial resolutions [36], frame rates [37], [38], dynamic ranges [28], exposure levels [39], focus points [40], color/gray tones [41], and viewing devices [42], that are beyond the major focus of the current work.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Performance Evaluation of Image Quality Assessment Algorithms

2019

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Image quality assessment (IQA) algorithms aim to predict perceived image quality by human observers. Over the last two decades, a large amount of work has been carried out in the field. New algorithms are being developed at a rapid rate in different areas of IQA, but are often tested and compared with limited existing models using out-of-date test data. There is a significant gap when it comes to large-scale performance evaluation studies that include a wide variety of test data and competing algorithms. In this work we aim to fill this gap by carrying out the largest performance evaluation study so far. We test the performance of 43 full-reference (FR), seven fused FR (22 versions), and 14 no-reference (NR) methods on nine subject-rated IQA datasets, of which five contain singly distorted images and four contain multiply distorted content. We use a variety of performance evaluation and statistical significance testing criteria. Our findings not only point to the top performing FR and NR IQA methods, but also highlight the performance gap between them. In addition, we have also conducted a comparative study on FR fusion methods, and an important discovery is that rank aggregation based FR fusion is able to outperform not only other FR fusion approaches but also the top performing FR methods. It may be used to annotate IQA datasets as a possible alternative to subjective ratings, especially in situations where it is not possible to obtain human opinions, such as in the case of large-scale datasets composed of thousands or even millions of images.

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Section: Introductionmentioning

confidence: 99%

A Comprehensive Performance Evaluation of Image Quality Assessment Algorithms

2019

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“…First, the design of our technique benefits from some principles. Image structure has an extremely important influence on the visual quality of human judgement, and it has been widely used in most existing IQA models [16][17][18][19][20][21][22][23][24][25][26][27][28] and some related applications [7][8][9][10][11][12][13]. In [29], the authors have pointed out that using a proper Gaussian kernel to filter the input original and distorted images can effectively extract image structure.…”

Section: Resultsmentioning

confidence: 99%

“…the mean opinion score (MOS). Subjective quality scores are quite valuable, because on the one hand they can be used to justify the performance of objective IQA algorithms (e.g., LIVE [1], TID2008 [2], CSIQ [3], IVC [4], Toyama [5] and TID2013 [6]), and on the other hand, they are able to instruct denoising [7], restoration [8], coding [9][10][11], and compare tone-mapped operators [12][13][14]. But subjective methods easily suffer the drawbacks of being laborious, costly and time-consuming, and thus cannot be adapted to real-time applications.…”

Section: Introductionmentioning

confidence: 99%

Content-weighted mean-squared error for quality assessment of compressed images

Wang

Zhai

et al. 2015

SIViP

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Image quality assessment (IQA) has been intensively studied, especially for the full-reference (FR) scenario. However, only the mean-squared error (MSE) is widely employed in compression. Why other IQA metrics work ineffectively? We first sum up three main limitations including the computational time, portability, and working manner.To address these problems, we then in this paper propose a new content-weighted MSE (CW-MSE) method to assess the quality of compressed images. The design principle of our model is to use adaptive Gaussian convolution to estimate the influence of image content in a block-based manner, thereby to approximate the human visual perception to image quality. Results of experiments on six popular subjective image quality databases (including LIVE, TID2008, CSIQ, IVC, Toyama and TID2013) confirm the superiority of our CW-MSE over state-of-the-art FR IQA approaches.

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“…The C2G-SSIM algorithm is designed to assess the quality of a gray scale image that is converted from a color image [11]. The WIND algorithm can evaluate the quality of an interpolated high-resolution natural image using a low-resolution image (which was used to create the high-resolution image based on an interpolation algorithm) as the reference image [46]. There has been a clear trend that more effort will be dedicated to these research directions in the future.…”

Section: The Real-world Challengesmentioning

confidence: 99%

Objective Image Quality Assessment: Facing The Real-World Challenges

Wang¹

2016

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There has been a growing interest in recent years in the development of objective image quality assessment (IQA) models, whose roles are not only to monitor image quality degradations and benchmark image processing systems, but also to optimize various image and video processing algorithms and systems. While the past achievement is worth celebrating, a number of major challenges remain when we apply existing IQA models in realworld applications. These include obvious ones such as the challenges to largely reduce the complexity of existing IQA algorithms and to make them easy-to-use and easy-to-understand. There are also challenges regarding the applicability of existing IQA models in many real-world problems where image quality needs to be evaluated and compared across dimensionality, across viewing environment, and across the form of representations − specific examples include quality assessment for image resizing, color-togray image conversion, multi-exposure image fusion, image retargeting, and high dynamic range image tone mapping. Here we will first elaborate these challenges, and then concentrate on a specific one, namely the generalization challenge, which we believe is a more fundamental issue in the development, validation and application of IQA models. Specifically, the challenge is about the generalization capability of existing IQA models, which achieve superior quality prediction performance in lab testing environment using a limited number of subject-rated test images, but the performance may not extend to the real-world where we are working with images of a much greater diversity in terms of content and complexity. We will discuss some principle ideas and related work that might help us meet the challenges in the future.

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Objective Quality Assessment of Interpolated Natural Images

Cited by 43 publications

References 35 publications

A Comprehensive Performance Evaluation of Image Quality Assessment Algorithms

A Comprehensive Performance Evaluation of Image Quality Assessment Algorithms

Content-weighted mean-squared error for quality assessment of compressed images

Objective Image Quality Assessment: Facing The Real-World Challenges

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