\({\mathrm {CB_{p}F}}\)-IQA: Using Contrast Band-Pass Filtering as Main Axis of Visual Image Quality Assessment

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.