Image quality assessment using two-dimensional complex mel-cepstrum

Abstract

Assessment of visual quality plays a crucial role in modeling, implementation, and optimization of image-and video-processing applications. The image quality assessment (IQA) techniques basically extract features from the images to generate objective scores. Feature-based IQA methods generally consist of two complementary phases: (1) feature extraction and (2) feature pooling. For feature extraction in the IQA framework, various algorithms have been used and recently, the two-dimensional (2-D) mel-cepstrum (2-DMC) feature extraction scheme has provided promising results in a feature-based IQA framework. However, the 2-DMC feature extraction scheme completely loses image-phase information that may contain high-frequency characteristics and important structural components of the image. In this work, "2-D complex mel-cepstrum" is proposed for feature extraction in an IQA framework. The method tries to integrate Fourier transform phase information into the 2-DMC, which was shown to be an efficient feature extraction scheme for assessment of image quality. Support vector regression is used for feature pooling that provides mapping between the proposed features and the subjective scores. Experimental results show that the proposed technique obtains promising results for the IQA problem by making use of the image-phase information.