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

Cataloged from PDF version of article.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 (20) 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. (C) 2011 Elsevier Ltd. All rights reserved

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

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. © 2011 Elsevier Ltd. All rights reserved

Gradient-based image and video quality assessment

У овој дисертацији разматране су објективне мере процене квалитета слике и видеа са потпуним и делимичним референцирањем на изворни сигнал. За потребе евалуације квалитета развијене су поуздане, рачунски ефикасне мере, засноване на очувању информација о градијенту. Мере су тестиране на великом броју тест слика и видео секвенци, различитих типова и степена деградације. Поред јавно доступних база слика и видео секвенци, за потребе истраживања формиране су и нове базе видео секвенци са преко 300 релевантних тест узорака. Поређењем доступних субјективних и објективних скорова квалитета показано је да је објективна евалуација квалитета веома сложен проблем, али га је могуће решити и доћи до високих перформанси коришћењем предложених мера процене квалитета слике и видеа.U ovoj disertaciji razmatrane su objektivne mere procene kvaliteta slike i videa sa potpunim i delimičnim referenciranjem na izvorni signal. Za potrebe evaluacije kvaliteta razvijene su pouzdane, računski efikasne mere, zasnovane na očuvanju informacija o gradijentu. Mere su testirane na velikom broju test slika i video sekvenci, različitih tipova i stepena degradacije. Pored javno dostupnih baza slika i video sekvenci, za potrebe istraživanja formirane su i nove baze video sekvenci sa preko 300 relevantnih test uzoraka. Poređenjem dostupnih subjektivnih i objektivnih skorova kvaliteta pokazano je da je objektivna evaluacija kvaliteta veoma složen problem, ali ga je moguće rešiti i doći do visokih performansi korišćenjem predloženih mera procene kvaliteta slike i videa.This thesis presents an investigation into objective image and video quality assessment with full and reduced reference on original (source) signal. For quality evaluation purposes, reliable, computational efficient, gradient-based measures are developed. Proposed measures are tested on different image and video datasets, with various types of distorsions and degradation levels. Along with publicly available image and video quality datasets, new video quality datasets are maded, with more than 300 relevant test samples. Through comparison between available subjective and objective quality scores it has been shown that objective quality evaluation is highly complex problem, but it is possible to resolve it and acchieve high performance using proposed quality measures