@inproceedings{42247807744d40b9b669ad25748fd478,
title = "No-reference image quality assessment based on visual codebook",
abstract = "In this paper, we propose a new learning based No-Reference Image Quality Assessment (NR-IQA) algorithm, which uses a visual codebook consisting of robust appearance descriptors extracted from local image patches to capture complex statistics of natural image for quality estimation. We use Gabor filter based local features as appearance descriptors and the codebook method encodes the statistics of natural image classes by vector quantizing the feature space and accumulating histograms of patch appearances based on this coding. This method does not assume any specific types of distortion and experimental results on the LIVE image quality assessment database show that this method provides consistent and reliable performance in quality estimation that exceeds other state-of-the-art NR-IQA approaches and is competitive with the full reference measure PSNR.",
keywords = "Gabor filter, no-reference image quality assessment, texture analysis, visual codebook",
author = "Peng Ye and David Doermann",
year = "2011",
doi = "10.1109/ICIP.2011.6116318",
language = "English",
isbn = "9781457713033",
series = "Proceedings - International Conference on Image Processing, ICIP",
pages = "3089--3092",
booktitle = "ICIP 2011",
note = "2011 18th IEEE International Conference on Image Processing, ICIP 2011 ; Conference date: 11-09-2011 Through 14-09-2011",
}