Download or read book A Deep Learning Approach for Blind Image Quality Assessment written by Maryam Pourebadi and published by . This book was released on 2017 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: High-resolution digital images are known as efficient carriers of information. Billions of them are distorted while being captured, stored, and shared. Thus, Image Quality Assessment (IQA) techniques are utilized to measure the quality of the source image in a way to match with subjective quality measured by human evaluation. Due to the lack of both reference image information and distortion type of the test image, Non-Distortion-Specific No-Reference (NDS NR) IQA focuses on improving assessment performance more intelligently. The idea of automatically obtaining features and weighting them without using reference images leads us to propose an innovative Deep Convolutional-Neural-Network (Deep CNN) architecture consisting of 17 layers to measure the quality of the NDS NR images. Our proposed Deep CNN employs both low-level and high-level features and provides more accurate performance, as well as noticeable improvement in computational time. The proposed network attains the best performance on Spearman Rank Order Correlation Coefficient (SRCC) evaluations among all popular NR-IQA methods listed in the paper. Furthermore, it achieves the highest Linear Correlation Coefficient (LCC) among all well-known No-Reference (NR) and Full-Reference (FR) IQA methods.