Survey on an Image Quality Assessment Metric based on Early Vision Features
B. Veeramallu1, Ch. LavanyaSusanna2, S. Sahitya3
1B.Veeramallu, department of computer science and engineering, KLUniversity.
2Ch.LavanyaSusanna, department of computer science and engineering, KLUniversity .
3S.Sahitya, department of computer science and engineering, KLUniversity
Manuscript received on January 01, 2013. | Revised Manuscript received on January 02, 2013. | Manuscript published on January 05, 2013. | PP: 447-449 | Volume-2, Issue-6, January 2013. | Retrieval Number: F1184112612/2013©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Evaluating the image perceptual quality is a fundamental problem in image and video processing, and various methods have been proposed for image quality assessment(IQA).This letter presents IQA metrics such as Conventional IQA indices ( mean squared error (MSE), signal-to-noise ratio (SNR) and peak signal-to-noise ratio (PSNR)), state-of-the-art IQA metrics(structural similarity based image quality assessment (SSIM),multi-scale-SSIM, non shift edge based ratio (NSER) and their limitations . In the non shift edge based ratio (NSER) method the procedures involved include computing the response of classical receptive fields, zero-crossing detection, and non-shift edge based ratio (NSER) calculation. This IQA metric is very simple but very effective and performs much better than most state-of-the-art IQA metric.
Keywords: Image quality assessment, structural similarity, non-shift edge, zero-crossing.