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Subrahmanyam CH
Technology and Research University
Computer Science & IT Electrical & Electronics Engineering

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Low bit Rate Video Quality Analysis Using NRDPF-VQA Algorithm Subrahmanyam CH; Venkata Rao D; Usha Rani N
International Journal of Electrical and Computer Engineering (IJECE) Vol 5, No 1: February 2015
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (224.392 KB) | DOI: 10.11591/ijece.v5i1.pp71-77

Abstract

In this work, we propose NRDPF-VQA (No Reference Distortion Patch Features Video Quality Assessment) model aims to use to measure the video quality assessment for H.264/AVC (Advanced Video Coding). The proposed method takes advantage of the contrast changes in the video quality by luminance changes. The proposed quality metric was tested by using LIVE video database. The experimental results show that the new index performance compared with the other NR-VQA models that require training on LIVE video databases, CSIQ video database, and VQEG HDTV video database. The values are compared with human score index analysis of DMOS.
Performance Analysis of No Reference Image quality based on Human Perception Subrahmanyam CH; D. Venkata Rao; N. Usha Rani
International Journal of Electrical and Computer Engineering (IJECE) Vol 4, No 6: December 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (300.966 KB)

Abstract

In this work, a No-Reference objective image quality assessment based on NRDPF-IQA metric and classification based metric are tested using LIVE database, which consisting of Gaussian white noise, Gaussian blur, Rayleigh fast fading channel, JPEG compressed images, JPEG2000 images. We plot the Spearman’s Rank Order Correlation Coefficient [SROCC] between each of these features and human DMOS from the LIVE-IQA database using our proposed method to ascertain how well the features correlate with human judgement quality. The analysis of the testing and training is done by SVM model. The proposed method shows better results compared with the earlier methods. Finally, the results are generated by using MATLAB.DOI:http://dx.doi.org/10.11591/ijece.v4i6.6783
Co-Authors D. Venkata Rao N. Usha Rani Usha Rani N Venkata Rao D