A Detail Based Method for Linear Full Reference Image Quality Prediction
In this paper, a novel Full Reference method is proposed for image quality assessment, using the combination of two separate metrics to measure the perceptually distinct impact of detail losses and of spurious details. To this purpose, the gradient of the impaired image is locally decomposed as a predicted version of the original gradient, plus a gradient residual. It is assumed that the detail attenuation identifies the detail loss, whereas the gradient residuals describe the spurious details. It turns out that the perceptual impact of detail losses is roughly linear with the loss of the positional Fisher information, while the perceptual impact of the spurious details is roughly proportional to a logarithmic measure of the signal to residual ratio. The affine combination of these two metrics forms a new index strongly correlated with the empirical Differential Mean Opinion Score (DMOS) for a significant class of image impairments, as verified for three independent popular databases. The method allowed alignment and merging of DMOS data coming from these different databases to a common DMOS scale by affine transformations. Unexpectedly, the DMOS scale setting is possible by the analysis of a single image affected by additive noise.
Code (0)
등록된 구현이 없습니다.
Tasks
Image Quality AssessmentSimilar Papers 제목 키워드 기반
Image Quality Assessment with Gradient Siamese Network
In this work, we introduce Gradient Siamese Network (GSN) for image quality assessment. The proposed method is skilled in capturing the gradient features between distorted images and reference images in full-reference im…
Full reference image quality assessmentFull-Reference Image Quality AssessmentImage Quality AssessmentStatistical evaluation of visual quality metrics for image denoising
This paper studies the problem of full reference visual quality assessment of denoised images with a special emphasis on images with low contrast and noise-like texture. Denoising of such images together with noise remov…
DenoisingImage DenoisingContent-Variant Reference Image Quality Assessment via Knowledge Distillation
Generally, humans are more skilled at perceiving differences between high-quality (HQ) and low-quality (LQ) images than directly judging the quality of a single LQ image. This situation also applies to image quality asse…
Image Quality AssessmentKnowledge DistillationNo-Reference Image Quality AssessmentReF-LDM: A Latent Diffusion Model for Reference-based Face Image Restoration
While recent works on blind face image restoration have successfully produced impressive high-quality (HQ) images with abundant details from low-quality (LQ) input images, the generated content may not accurately reflect…
Image RestorationDehazed Image Quality Evaluation: From Partial Discrepancy to Blind Perception
Image dehazing aims to restore spatial details from hazy images. There have emerged a number of image dehazing algorithms, designed to increase the visibility of those hazy images. However, much less work has been focuse…
Image Dehazing