Open AccessBlind Image Quality Assessment Using a CNN and Edge Distortion
Author(s) -
Rajesh Babu Movva,
Kontham Raja Kumar
Publication year - 2021
Publication title -
revue d'intelligence artificielle
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.146
H-Index - 14
eISSN - 1958-5748
pISSN - 0992-499X
DOI - 10.18280/ria.350406
Subject(s) - artificial intelligence , computer science , convolutional neural network , image quality , pattern recognition (psychology) , feature (linguistics) , pooling , image (mathematics) , prewitt operator , feature extraction , distortion (music) , computer vision , pyramid (geometry) , kernel (algebra) , enhanced data rates for gsm evolution , image processing , edge detection , mathematics , amplifier , computer network , philosophy , linguistics , geometry , bandwidth (computing) , combinatorics
The present paper introduces a Convolutional Neural Network (CNN) for the assessment of image quality without a reference image, which comes under the category of Blind Image Quality Assessment models. Edge distortions in the image are characterized as input feature vectors. This approach is in justification of the fact that subjective assessment focusses on image features that emanate from the edges and the boundaries present in the image. The earlier methods were found to use complex transformations on the image to extract the features before training or as a part of the training. The present work uses Prewitt kernel approach to extract the horizontal and vertical edge maps of the training images. These maps are then input to a simple CNN for extracting higher level features using non-linear transformations. The resultant features are mapped to image quality score by regression. The network uses Spatial Pyramid Pooling (SPP) layer to accommodate input images of varying sizes. The present proposed model was tested on popular datasets used in the domain of Image Quality Assessment (IQA). The experimental results have shown that the model competes with the earlier proposed models with simplicity of feature extraction and involvement of minimal complexity.