Evolutionary circular-ELM for the reduced-reference assessment of perceived image quality

Abstract

At present, the quality of the image is very important. The audience needs to get the undistorted image like the original image. Cause of the loss of image quality such as storage, transmission, compression and rendering. The mechanisms rely on systems that can assess the visual quality with human perception are required. Computational Intelligence (CI) paradigms represent a suitable technology to solve this challenging problem. In this paper present, the Evolutionary Extreme Learning Machine (EC-ELM) is derived into Circular-ELM (C-ELM) that is an extended Extreme Learning Machine (ELM) and the Differential Evolution (DE) to select appropriate weights and hidden biases, which can proves performance in addressing the visual quality assessment problem by embedded in the proposed framework. The experimental results, the EC-ELM can map the visual signals into quality score values that close to the real quality score than ELM, Evolutionary Extreme Learning (E-ELM) and the original C-ELM and also stable as well. Its can confirms that the EC-ELM is proved on recognized benchmarks and for four different types of distortions.