Scalable image quality assessment with 2D mel-cepstrum and machine learning approach

Date
2011-07-19
Advisor
Instructor
Source Title
Pattern Recognition
Print ISSN
0031-3203
Electronic ISSN
Publisher
Elsevier
Volume
45
Issue
1
Pages
299 - 313
Language
English
Type
Article
Journal Title
Journal ISSN
Volume Title
Abstract

Measurement of image quality is of fundamental importance to numerous image and video processing applications. Objective image quality assessment (IQA) is a two-stage process comprising of the following: (a) extraction of important information and discarding the redundant one, (b) pooling the detected features using appropriate weights. These two stages are not easy to tackle due to the complex nature of the human visual system (HVS). In this paper, we first investigate image features based on two-dimensional (2D) mel-cepstrum for the purpose of IQA. It is shown that these features are effective since they can represent the structural information, which is crucial for IQA. Moreover, they are also beneficial in a reduced-reference scenario where only partial reference image information is used for quality assessment. We address the second issue by exploiting machine learning. In our opinion, the well established methodology of machine learning/pattern recognition has not been adequately used for IQA so far; we believe that it will be an effective tool for feature pooling since the required weights/parameters can be determined in a more convincing way via training with the ground truth obtained according to subjective scores. This helps to overcome the limitations of the existing pooling methods, which tend to be over simplistic and lack theoretical justification. Therefore, we propose a new metric by formulating IQA as a pattern recognition problem. Extensive experiments conducted using six publicly available image databases (totally 3211 images with diverse distortions) and one video database (with 78 video sequences) demonstrate the effectiveness and efficiency of the proposed metric, in comparison with seven relevant existing metrics.

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Other identifiers
Book Title
Keywords
2D mel-cepstral features, Feature extraction, Image quality assessment, Machine learning, 2D mel-cepstral features, Complex nature, Effective tool, Ground truth, Human visual systems, Image and video processing, Image database, Image features, Image quality assessment, Machine-learning, Objective image quality assessment, Pattern recognition problems, Quality assessment, Reduced reference, Reference image, Scalable image, Structural information, Two stage, Two-stage process, Video database, Video sequences, Feature extraction, Learning systems, Rating, Video recording, Image quality
Citation
Published Version (Please cite this version)