To provide the possibility of developing objective image quality assessment (IQA) algorithms for THz security images, we constructed the THz security image database (THSID) including a total of 181 THz security images with the resolution of 127×380. The main distortion types in THz security images were first analyzed for the design of subjective evaluation criteria to acquire the mean opinion s...

Evaluation and optimization of visual quality is of fundamental importance to numerous image and video processing applications including broadcasting, archiving, and restoration. As quality assessment using subjective tests is very costly and time-consuming, objective measures are required that can predict the perceptual judgment of human viewers. In this paper, a topdown full-reference quality...

The state-of-the-art pooling strategies for perceptual image quality assessment (IQA) are based on the mean and the weighted mean. They are robust pooling strategies which usually provide a moderate to high performance for different IQAs. Recently, standard deviation (SD) pooling was also proposed. Although, this deviation pooling provides a very high performance for a few IQAs, its performance...

No-reference image quality assessment (NR-IQA) is a fundamental yet challenging task in low-level computer vision community. The difficulty is particularly pronounced for the limited information, for which the corresponding reference for comparison is typically absent. Although various feature extraction mechanisms have been leveraged from natural scene statistics to deep neural networks in pre...

A crucial step in image compression is the evaluation of its performance, and more precisely, available ways to measure the quality of compressed images. In this paper, based on a learned classi , fication process in order to respect human observers a method namely Machine Learning-based Image Quality Measure (MLIQM) is proposed,which classifies the quality using multi-Support Vector Machine (S...

This paper presents a novel sharpness metric for color images. The proposed metric can be used for no-reference assessment of image visual quality. The metric basically relies on local power of wavelet transform high-frequency coefficients. It also takes into account a possibility of the presence of macrophotography and portrait photography effects in an image where the image part (usually cent...

In this paper, we present a novel no-reference (NR) method to assess the quality of JPEG-coded images using a sequential learning algorithm for growing and pruning radial basis function (GAP-RBF) network. The features for predicting the perceived image quality are extracted by considering key human visual sensitivity factors such as edge amplitude, edge length, background activity and backgroun...

Inspired by the free-energy brain theory (Friston, Kilner, and Harrison 2006), which implies that human visual system (HVS) tends to reduce uncertainty and restore perceptual details upon seeing a distorted image (Friston 2010), we propose restorative adversarial net (RAN), a GAN-based model for no-reference image quality assessment (NR-IQA). RAN, which mimics the process of HVS, consists of th...

In image and video denoising, a quantitative measure of genuine image content, noise, and blur is required to facilitate quality assessment, when the ground-truth is not available. In this paper, we present a no-reference image quality assessment for denoising applications, that examines local image structure using orientation dominancy and patch sparsity. We propose a fast method to find the d...

In this contribution, different image quality assessment methods are compared. Their basic principles are introduced and evaluation results are given. 1. Introduction Image and video quality measures play an important role in a variety of image and video processing applications. Very often the quality of an image needs to be quantified. This can be done by subjective testing sessions, or by obj...