Abstract
Our proposal is to present a Blind and Reference Image Quality Assessment or CBPF-IQA. Thus, the main proposal of this paper is to propose an Interface, which contains not only a Full-Reference Image Quality Assessment (IQA) but also a No-Reference or Blind IQA applying perceptual concepts by means of Contrast Band-Pass Filtering (CBPF). Then, this proposal consists, in contrast, a degraded input image with the filtered versions of several distances by a CBPF, which computes some of the Human Visual System (HVS) variables. If CBPF-IQA detects only one input, it performs a Blind Image Quality Assessment, on the contrary, if CBPF-IQA detects two inputs, it considers that a Reference Image Quality Assessment will be computed. Thus, we first define a Full-Reference IQA and then a No-Reference IQA, which correlation is important when is contrasted with the psychophysical results performed by several observers. CBPF-IQA weights the Peak Signal-to-Noise Ratio by using an algorithm that estimates some properties of the Human Visual System. Then, we compare \({\mathrm {CB_{p}F}}\)-IQA algorithm not only with the mainstream estimator in IQA and PSNR but also state-of-the-art IQA algorithms, such as Structural SIMilarity (SSIM), Mean Structural SIMilarity (MSSIM), and Visual Information Fidelity (VIF). Our experiments show that the correlation of CBPF-IQA correlated with PSNR is important, but this proposal does not need imperatively the reference image in order to estimate the quality of the recovered image.
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Acknowledgements
This work is supported by National Polytechnic Institute of Mexico (Instituto Politécnico Nacional, México) by means of Project No. SIP-20171179, the Academic Secretary and the Committee of Operation and Promotion of Academic Activities (COFAA) and National Council of Science and Technology of Mexico (CONACyT).
It is important to mention that Sects. 4 and 5 are part of the degree thesis supported by Eduardo García and Yasser Sánchez.
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Moreno-Escobar, J.J., Martínez-González, C.L., Morales-Matamoros, O., Tejeida-Padilla, R. (2018). \({\mathrm {CB_{p}F}}\)-IQA: Using Contrast Band-Pass Filtering as Main Axis of Visual Image Quality Assessment. In: Hassanien, A., Oliva, D. (eds) Advances in Soft Computing and Machine Learning in Image Processing. Studies in Computational Intelligence, vol 730. Springer, Cham. https://doi.org/10.1007/978-3-319-63754-9_29
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