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

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Abstract

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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Notes

  1. This statement will be explicitly analyzed in Sect. 3.

  2. Interested readers can be directed to [35] for more recent IQA studies.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, N2L 3G1, Canada

    Ke Gu & Shiqi Wang

  2. Institute of Image Communication and Information Processing, Shanghai Jiao Tong University, Shanghai, 200240, China

    Ke Gu, Guangtao Zhai, Xiaokang Yang & Wenjun Zhang

  3. Shanghai Key Laboratory of Digital Media Processing and Transmissions, Shanghai, China

    Ke Gu, Guangtao Zhai, Xiaokang Yang & Wenjun Zhang

  4. Institute of Digital Media, Peking University, Beijing, 100871, China

    Siwei Ma

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  1. Ke Gu
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  2. Shiqi Wang
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  3. Guangtao Zhai
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  4. Siwei Ma
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Correspondence to Ke Gu.

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Gu, K., Wang, S., Zhai, G. et al. Content-weighted mean-squared error for quality assessment of compressed images. SIViP 10, 803–810 (2016). https://doi.org/10.1007/s11760-015-0818-9

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