Referenceless Image Quality Assessment Utilizing Deep Transfer-Learned Features

Identificador:  imarina:9385563

Handle: https://hdl.handle.net/20.500.11797/imarina9385563

Autors:  Ahmed, Basma; Omer, Osama A; Rashed, Amal; Puig, Domenec; Abdel-Nasser, Mohamed

Resum:
Image quality assessment (IQA) algorithms are critical for determining the quality of high-resolution photographs. This work proposes a hybrid NR IQA approach that uses deep transfer learning to enhance classic NR IQA with deep learning characteristics. Firstly, we simulate a pseudo reference image (PRI) from the input image. Then, we used a pre-trained inception-v3 deep feature extractor to generate the feature maps from the input distorted image and PRI. The distance between the feature maps of the input distorted image and PRI are measured using the local structural similarity (LSS) method. A nonlinear mapping function is used to calculate the final quality scores. When compared to previous work, the proposed method has a promising performance.