Total Variation Based Perceptual Image Quality Assessment Modeling

Image Quality Assessment based on Perceptual Blur Metric

The recent development of digital image acquisition technologies leads to better image quality, in terms of spatial resolution and sensitivity. Image quality is a characteristic of an image that measures the perceived image degradation. Several techniques and metrics are proposed which can be classified as Full-Reference (FR) method, No-Reference (NR) method and Reduced Reference (RR) method. I...

A measure for perceptual image quality assessment

This paper addresses the issue of perceptual image quality assessment in image restoration systems. Firstly, experiments are conducted to evaluate perceived quality in images degraded only by frequency distortion. Based on the resulting experimental data, a distortion quality measure (DQM) is proposed. Then another experiment, whose test images present frequency distortion and noise injection, ...

From Image Quality Assessment to Perceptual Quality Preserved Image Restoration: A Perceptual Transform Learning

Image authentication using LBP-based perceptual image hashing

Feature extraction is a main step in all perceptual image hashing schemes in which robust features will led to better results in perceptual robustness. Simplicity, discriminative power, computational efficiency and robustness to illumination changes are counted as distinguished properties of Local Binary Pattern features. In this paper, we investigate the use of local binary patterns for percep...

Image Quality Assessment Based on Intrinsic Mode Function Coefficients Modeling

Reduced reference image quality assessment (RRIQA) methods aim to assess the quality of a perceived image with only a reduced cue from its original version, called ”reference image”. The powerful advantage of RR methods is their ”General-purpose”. However, most introduced RR methods are built upon a non-adaptive transform models. This can limit the scope of RR methods to a small number of disto...