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Gradient magnitude similarity deviation: A highly efficient perceptual image quality index
- Image Processing, IEEE Transactions on
, 2014
"... Abstract — It is an important task to faithfully evaluate the perceptual quality of output images in many applications, such as image compression, image restoration, and multimedia stream-ing. A good image quality assessment (IQA) model should not only deliver high quality prediction accuracy, but a ..."
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Cited by 9 (1 self)
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Abstract — It is an important task to faithfully evaluate the perceptual quality of output images in many applications, such as image compression, image restoration, and multimedia stream-ing. A good image quality assessment (IQA) model should not only deliver high quality prediction accuracy, but also be com-putationally efficient. The efficiency of IQA metrics is becoming particularly important due to the increasing proliferation of high-volume visual data in high-speed networks. We present a new effective and efficient IQA model, called gradient magnitude similarity deviation (GMSD). The image gradients are sensitive to image distortions, while different local structures in a distorted image suffer different degrees of degradations. This motivates us to explore the use of global variation of gradient based local quality map for overall image quality prediction. We find that the pixel-wise gradient magnitude similarity (GMS) between the reference and distorted images combined with a novel pooling strategy—the standard deviation of the GMS map—can predict accurately perceptual image quality. The resulting GMSD algorithm is much faster than most state-of-the-art IQA methods, and delivers highly competitive prediction accuracy. MATLAB source code of GMSD can be downloaded at
Reduced-Reference Image Quality Assessment by Structural Similarity Estimation
, 2012
"... Reduced-reference image quality assessment (RR-IQA) provides a practical solution for automatic image quality evaluations in various applications where only partial information about the original reference image is accessible. In this paper, we propose an RR-IQA method by estimating the structural ..."
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Cited by 5 (2 self)
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Reduced-reference image quality assessment (RR-IQA) provides a practical solution for automatic image quality evaluations in various applications where only partial information about the original reference image is accessible. In this paper, we propose an RR-IQA method by estimating the structural similarity index (SSIM), which is a widely used full-reference (FR) image quality measure shown to be a good indicator of perceptual image quality. Specifically, we extract statistical features from a multiscale multiorientation divisive normalization transform and develop a distortion measure by following the philosophy in the construction of SSIM. We find an interesting linear relationship between the FR SSIM measure and our RR estimate when the image distortion type is fixed. A regression-by-discretization method is then applied to normalize our measure across image distortion types. We use six publicly available subject-rated databases to test the proposed RR-SSIM method, which shows strong correlations with both SSIM and subjective quality evaluations. Finally, we introduce the novel idea of partially repairing an image using RR features and use deblurring as an example to demonstrate its application.
Perceptual video coding based on ssim-inspired divisive normalization,” Image Processing
- IEEE Transactions on
, 2013
"... Abstract — We propose a perceptual video coding framework based on the divisive normalization scheme, which is found to be an effective approach to model the perceptual sensitivity of biological vision, but has not been fully exploited in the context of video coding. At the macroblock (MB) level, we ..."
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Cited by 4 (3 self)
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Abstract — We propose a perceptual video coding framework based on the divisive normalization scheme, which is found to be an effective approach to model the perceptual sensitivity of biological vision, but has not been fully exploited in the context of video coding. At the macroblock (MB) level, we derive the normalization factors based on the structural similarity (SSIM) index as an attempt to transform the discrete cosine transform domain frame residuals to a perceptually uniform space. We fur-ther develop an MB level perceptual mode selection scheme and a frame level global quantization matrix optimization method. Extensive simulations and subjective tests verify that, compared with the H.264/AVC video coding standard, the proposed method can achieve significant gain in terms of rate-SSIM performance and provide better visual quality. Index Terms — Divisive normalization, H.264/AVC coding, perceptual video coding, rate distortion optimization, structural similarity (SSIM) index. I.
SSIM-Inspired Perceptual Video Coding for HEVC
"... Abstract—Recent advances in video capturing and display technologies, along with the exponentially increasing demand of video services, challenge the video coding research community to design new algorithms able to significantly improve the compression performance of the current H.264/AVC standard. ..."
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Abstract—Recent advances in video capturing and display technologies, along with the exponentially increasing demand of video services, challenge the video coding research community to design new algorithms able to significantly improve the compression performance of the current H.264/AVC standard. This target is currently gaining evidence with the standardization activities in the High Efficiency Video Coding (HEVC) project. The distortion models used in HEVC are mean squared error (MSE) and sum of absolute difference (SAD). However, they are widely criticized for not correlating well with perceptual image quality. The structural similarity (SSIM) index has been found to be a good indicator of perceived image quality. Meanwhile, it is computationally simple compared with other state-of-the-art perceptual quality measures and has a number of desirable mathematical properties for optimization tasks. We propose a perceptual video coding method to improve upon the current HEVC based on an SSIM-inspired divisive normalization scheme as an attempt to transform the DCT domain frame prediction residuals to a perceptually uniform space before encoding. Based on the residual divisive normalization process, we define a distortion model for mode selection and show that such a divisive normalization strategy largely simplifies the subsequent perceptual rate-distortion optimization procedure. We further adjust the divisive normalization factors based on local content of the video frame. Experiments show that the proposed scheme can achieve significant gain in terms of rate-SSIM performance when compared with HEVC. Index Terms—SSIM index; HEVC; rate distortion optimiza-tion; residual divisive normalization; I.
APPROVAL
, 2013
"... may be reproduced, without authorization, under the conditions for Fair Dealing. Therefore, limited reproduction of this work for the purposes of private study, research, criticism, review and news reporting is likely to be in accordance with the law, particularly if cited appropriately. ..."
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may be reproduced, without authorization, under the conditions for Fair Dealing. Therefore, limited reproduction of this work for the purposes of private study, research, criticism, review and news reporting is likely to be in accordance with the law, particularly if cited appropriately.
1 Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index
"... Abstract – Faithfully evaluating perceptual image quality is an important task in applications such as image compression, image restoration and multimedia streaming. A good image quality assessment (IQA) model is expected to be not only effective (i.e., deliver high quality prediction accuracy) but ..."
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Abstract – Faithfully evaluating perceptual image quality is an important task in applications such as image compression, image restoration and multimedia streaming. A good image quality assessment (IQA) model is expected to be not only effective (i.e., deliver high quality prediction accuracy) but also computationally efficient. Owing to the need to deploy image quality measurement tools in high-speed networks, the efficiency of an IQA metric is particularly important due to the increasing proliferation of high-volume visual data. Here we develop and explain a new effective and efficient IQA model, called gradient magnitude similarity deviation (GMSD). Although the image gradient has been employed in other IQA models, few have achieved favorable performance in terms of both accuracy and efficiency. The results are proactive: we find that the pixel-wise gradient magnitude similarity (GMS) between the reference and distorted images combined with a novel pooling strategy – the standard deviation of the GMS map – predict accurately perceptual image quality. The resulting GMSD algorithm is much faster than most state-of-the-art IQA methods, and delivers highly competitive prediction accuracy on benchmark IQA databases. Matlab code that implements GMSD can be downloaded at
Objective Quality Assessment for Color-to-Gray Image Conversion
"... Abstract — Color-to-gray (C2G) image conversion is the process of transforming a color image into a grayscale one. Despite its wide usage in real-world applications, little work has been dedicated to compare the performance of C2G conversion algo-rithms. Subjective evaluation is reliable but is also ..."
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Abstract — Color-to-gray (C2G) image conversion is the process of transforming a color image into a grayscale one. Despite its wide usage in real-world applications, little work has been dedicated to compare the performance of C2G conversion algo-rithms. Subjective evaluation is reliable but is also inconvenient and time consuming. Here, we make one of the first attempts to develop an objective quality model that automatically predicts the perceived quality of C2G converted images. Inspired by the philosophy of the structural similarity index, we propose a C2G structural similarity (C2G-SSIM) index, which evaluates the luminance, contrast, and structure similarities between the reference color image and the C2G converted image. The three components are then combined depending on image type to yield an overall quality measure. Experimental results show that the proposed C2G-SSIM index has close agreement with subjective rankings and significantly outperforms existing objective quality metrics for C2G conversion. To explore the potentials of C2G-SSIM, we further demonstrate its use in two applications: 1) automatic parameter tuning for C2G conversion algorithms and 2) adaptive fusion of C2G converted images. Index Terms — Image quality assessment, color-to-gray conversion, perceptual image processing, structural similarity.
On the Use of SSIM in HEVC
"... Abstract—The Structural SIMilarity (SSIM) index has been attracting an increasing amount of attention recently in the video coding community as a perceptual criterion for testing and opti-mizing video codecs. Meanwhile, the arrival of the new MPEG-H/H.265 High Efficiency Video Coding (HEVC) standard ..."
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Abstract—The Structural SIMilarity (SSIM) index has been attracting an increasing amount of attention recently in the video coding community as a perceptual criterion for testing and opti-mizing video codecs. Meanwhile, the arrival of the new MPEG-H/H.265 High Efficiency Video Coding (HEVC) standard creates new opportunities and challenges in perceptual video coding. In this paper, we first elaborate what are the attributes that make SSIM a good candidate for perception-based development of HEVC and future video coding standards for both testing and optimization purposes. We then address the computational issues in practical applications of SSIM in HEVC, in particular the trade-off between efficient computation and accurate estimation of SSIM when working with video codecs that have sophisticated block partitioning structures and aim for encoding videos with a wide range of spatial resolutions. Keywords—video coding, high efficiency video coding (HEVC), H.265, MPEG-H, video quality assessment, structure similarity (SSIM), perceptual optimization, perceptual video coding I.
SSIM-Based Coarse-Grain Scalable Video Coding
"... Abstract—We propose an improved coarse-grain scalable video coding (SVC) approach based on the structural sim-ilarity (SSIM) index as the visual quality criterion, aiming at maximizing the overall coding performance constrained by user-defined quality weightings for all scalable layers. First, we de ..."
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Abstract—We propose an improved coarse-grain scalable video coding (SVC) approach based on the structural sim-ilarity (SSIM) index as the visual quality criterion, aiming at maximizing the overall coding performance constrained by user-defined quality weightings for all scalable layers. First, we develop an interlayer rate-SSIM dependency model, by investigating bit rate and SSIM relationships between differ-ent layers. Second, a reduced-reference SSIM-Q model and a Laplacian R-Q model are introduced for SVC, by incorporat-ing the characteristics of hierarchical prediction structure in each layer. Third, based on the user-defined weightings and the proposed models, we design a rate-distortion optimization approach to adaptively adjust Lagrange multipliers for all lay-ers to maximize the overall rate-SSIM performance of the scalable encoder. Experiments with multiple layers, different layer weightings, and various videos demonstrate that the pro-posed framework can achieve better rate-SSIM performance than single layer optimization method, and provide better cod-ing efficiency as compared to the conventional SVC scheme. Subjective tests further demonstrate the benefits of the proposed scheme. Index Terms—Scalable video coding (SVC), coarse-grain scalability (CGS), structural similarity (SSIM), rate-distortion optimization (RDO), Lagrange multiplier (LM). I.
1 Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index
"... Abstract – It is an important task to faithfully evaluate the perceptual quality of output images in many applications such as image compression, image restoration and multimedia streaming. A good image quality assessment (IQA) model should not only deliver high quality prediction accuracy but also ..."
Abstract
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Abstract – It is an important task to faithfully evaluate the perceptual quality of output images in many applications such as image compression, image restoration and multimedia streaming. A good image quality assessment (IQA) model should not only deliver high quality prediction accuracy but also be computationally efficient. The efficiency of IQA metrics is becoming particularly important due to the increasing proliferation of high-volume visual data in high-speed networks. We present a new effective and efficient IQA model, called gradient magnitude similarity deviation (GMSD). The image gradients are sensitive to image distortions, while different local structures in a distorted image suffer different degrees of degradations. This motivates us to explore the use of global variation of gradient based local quality map for overall image quality prediction. We find that the pixel-wise gradient magnitude similarity (GMS) between the reference and distorted images combined with a novel pooling strategy – the standard deviation of the GMS map – can predict accurately perceptual image quality. The resulting GMSD algorithm is much faster than most state-of-the-art IQA methods, and delivers highly competitive prediction accuracy. MATLAB source code of GMSD can be downloaded at
