Dr. Chaofeng Li Profile

Dr. Chaofeng Li

at Jiangnan Univ

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Publications (5)

SPIE Journal Paper | 1 September 2015

Video quality assessment using content-weighted spatial and temporal pooling method

Chaofeng Li, Feng Pan, Xiaojun Wu, Yiwen Ju, Yun-Hao Yuan, Wei Fang

JEI, Vol. 24, Issue 05, 053001, (September 2015) https://doi.org/10.1117/12.10.1117/1.JEI.24.5.053001

KEYWORDS: Video, Databases, Distortion, Video compression, Image quality, Video processing, Volume rendering, Lithium, Visualization, Statistical modeling

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SPIE Journal Paper | 25 August 2014

Universal blind image quality assessment using contourlet transform and singular-value decomposition

Qingbing Sang, Xiaojun Wu, Chaofeng Li, Yin Lu

JEI, Vol. 23, Issue 06, 061104, (August 2014) https://doi.org/10.1117/12.10.1117/1.JEI.23.6.061104

KEYWORDS: Image quality, Databases, Statistical modeling, Nickel, Image processing, Algorithm development, Image segmentation, Image analysis, Image filtering, Molybdenum

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SPIE Journal Paper | 7 May 2013

No-training, no-reference image quality index using perceptual features

Chaofeng Li, Yiwen Ju, Alan Bovik, Xiaojun Wu, Qingbing Sang

OE, Vol. 52, Issue 05, 057003, (May 2013) https://doi.org/10.1117/12.10.1117/1.OE.52.5.057003

KEYWORDS: Image quality, Distortion, Databases, Statistical modeling, Optical engineering, Data modeling, Machine learning, Phase measurement, Video, Image compression

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SPIE Journal Paper | 1 January 2010

Content-weighted video quality assessment using a three-component image model

Chaofeng Li, Alan Bovik

JEI, Vol. 19, Issue 01, 011003, (January 2010) https://doi.org/10.1117/12.10.1117/1.3267087

KEYWORDS: Image quality, Video, Volume rendering, Visualization, Video compression, Databases, MATLAB, Image enhancement, Distortion, Motion models

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Proceedings Article | 19 January 2009 Paper

Three-component weighted structural similarity index

Chaofeng Li, Alan Bovik

Proceedings Volume 7242, 72420Q (2009) https://doi.org/10.1117/12.811821

KEYWORDS: Image quality, Visualization, Image segmentation, Distortion, Image compression, Volume rendering, Databases, Image processing, Algorithm development, JPEG2000

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The assessment of image quality is very important for numerous image processing applications, where the goal of image quality assessment (IQA) algorithms is to automatically assess the quality of images in a manner that is consistent with human visual judgment. Two prominent examples, the Structural Similarity Image Metric (SSIM) and Multi-scale Structural Similarity (MS-SSIM) operate under the assumption that human visual perception is highly adapted for extracting structural information from a scene. Results in large human studies have shown that these quality indices perform very well relative to other methods. However, the performance of SSIM and other IQA algorithms are less effective when used to rate amongst blurred and noisy images. We address this defect by considering a three-component image model, leading to the development of modified versions of SSIM and MS-SSIM, which we call three component SSIM (3-SSIM) and three component MS-SSIM (3-MS-SSIM). A three-component image model was proposed by Ran and Farvardin, ^[13] wherein an image was decomposed into edges, textures and smooth regions. Different image regions have different importance for vision perception, thus, we apply different weights to the SSIM scores according to the region where it is calculated. Thus, four steps are executed: (1) Calculate the SSIM (or MS-SSIM) map. (2) Segment the original (reference) image into three categories of regions (edges, textures and smooth regions). Edge regions are found where a gradient magnitude estimate is large, while smooth regions are determined where the gradient magnitude estimate is small. Textured regions are taken to fall between these two thresholds. (3) Apply non-uniform weights to the SSIM (or MS-SSIM) values over the three regions. The weight for edge regions was fixed at 0.5, for textured regions it was fixed at 0.25, and at 0.25 for smooth regions. (4) Pool the weighted SSIM (or MS-SSIM) values, typically by taking their weighted average, thus defining a single quality index for the image (3-SSIM or 3-MS-SSIM). Our experimental results show that 3-SSIM (or 3-MS-SSIM) provide results consistent with human subjectivity when finding the quality of blurred and noisy images, and also deliver better performance than SSIM (and MS-SSIM) on five types of distorted images from the LIVE Image Quality Assessment Database.

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