Perceptual image quality assessment using a geometric structural distortion model

Cheng, Guangquan; Huang, Jincai; Zhu, Cheng; Liu, Zhong; Cheng, Lizhi

doi:10.1109/icip.2010.5649265

Cited by 30 publications

(28 citation statements)

References 8 publications

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“…However, HVS is too complex and our current knowledge about it is far from enough to construct an accurate bottom-up IQA framework. The second strategy adopts a top-down framework [3,30,[4][5][6][7][8], which aims to model the overall function of HVS based on some global assumptions on it. Many FR-IQA models follow this framework.…”

Section: Introductionmentioning

confidence: 99%

“…Other state-of-the-art FR-IQA models that follow the top-down framework include Ratio of Non-shift Edges (rNSE) [18,24], Feature SIMilarity (FSIM) [7] comprehensive survey and comparison of state-of-the-art IQA models can be found in [30,14]. Aside from the two different strategies for FR-IQA model design, many IQA models share a common two-step framework [16,[4][5][6][7][8], as illustrated in Fig. 1.…”

Section: Introductionmentioning

confidence: 99%

“…The most commonly encountered image distortions, including noise corruption, blur and compression artifacts, will lead to highly visible structural changes that "pop out" of the gradient domain. Most gradient based FR-IQA models [5][6][7]15] were inspired by SSIM [8]. They first compute the similarity between the gradients of reference and distorted images, and then compute some additional information, such as the difference of gradient orientation, luminance similarity and phase congruency similarity, to combine with the gradient similarity for pooling.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index

Xue

Zhang

Mou

et al. 2014

IEEE Trans. on Image Process.

1,324

644

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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 http://www4.comp.polyu.edu.hk/~cslzhang/IQA/ GMSD/GMSD.htm.Index Terms-Gradient magnitude similarity, image quality assessment, standard deviation pooling, full reference

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index

Xue

Zhang

Mou

et al. 2014

IEEE Trans. on Image Process.

1,324

644

View full text Add to dashboard Cite

show abstract

“…Nevertheless, HVS is too intricate to construct an accurate bottom-up FR-IQA framework. The second one constructs a top-down framework [8][9][10][11] which designs to model the overall function of HVS according to some global assumption. Recent studies [8,9] have demonstrated the effectiveness of these kinds of methods, and thus, many approaches follow the top-down framework.…”

Section: Introductionmentioning

confidence: 99%

GMVP: gradient magnitude and variance pooling-based image quality assessment in sensor networks

Zhang

Liu

2016

J Wireless Com Network

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In this paper, we focus on image quality assessment (IQA) in sensor networks and propose a novel method named gradient magnitude and variance pooling (GMVP). The proposed GMVP follows a two-step framework. In this first step, we utilize gradient magnitude to compute the local quality, which is efficient and responsive to degeneration when the images are transmitted by sensor networks. In the second step, we propose a weighted pooling operation , i.e., variance pooling, which explicitly considers the importance of different local regions. The variance pooling operation assigns different weights to local quality map according to the variance of local regions. The proposed GMVP is verified on two challenging IQA databases (CSIQ and TID 2008 databases), and the results demonstrate that the proposed GMVP achieves better results than the state-of-the-art methods in sensor networks.

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“…In recent years, a great deal of researches on image compression have been carried out, and many image coding technologies and standards have been developed [4][5][6][7]. These coding techniques mainly involve the image compression technology based on the human visual and auditory characteristics, wavelet transform theory, and fractal theory [8][9][10][11][12][13]. However, the contrast sensitivity characteristics of the human visual system (HVS) have not been exploited much in the field of color image compression [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

A novel color image compression algorithm using the human visual contrast sensitivity characteristics

Jun-cai

Liu

2016

Photonic Sens

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Abstract:In order to achieve higher image compression ratio and improve visual perception of the decompressed image, a novel color image compression scheme based on the contrast sensitivity characteristics of the human visual system (HVS) is proposed. In the proposed scheme, firstly the image is converted into the YC r C b color space and divided into sub-blocks. Afterwards, the discrete cosine transform is carried out for each sub-block, and three quantization matrices are built to quantize the frequency spectrum coefficients of the images by combining the contrast sensitivity characteristics of HVS. The Huffman algorithm is used to encode the quantized data. The inverse process involves decompression and matching to reconstruct the decompressed color image. And simulations are carried out for two color images. The results show that the average structural similarity index measurement (SSIM) and peak signal to noise ratio (PSNR) under the approximate compression ratio could be increased by 2.78% and 5.48%, respectively, compared with the joint photographic experts group (JPEG) compression. The results indicate that the proposed compression algorithm in the text is feasible and effective to achieve higher compression ratio under ensuring the encoding and image quality, which can fully meet the needs of storage and transmission of color images in daily life.

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Perceptual image quality assessment using a geometric structural distortion model

Cited by 30 publications

References 8 publications

Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index

Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index

GMVP: gradient magnitude and variance pooling-based image quality assessment in sensor networks

A novel color image compression algorithm using the human visual contrast sensitivity characteristics

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