A model utilizing artificial neural network for perceptual image quality assessment in image compression algorithms

Karel Fliegel

doi:10.1117/12.679564

25 August 2006 A model utilizing artificial neural network for perceptual image quality assessment in image compression algorithms

Karel Fliegel

Author Affiliations +

Proceedings Volume 6315, Mathematics of Data/Image Pattern Recognition, Compression, and Encryption with Applications IX; 631507 (2006) https://doi.org/10.1117/12.679564
Event: SPIE Optics + Photonics, 2006, San Diego, California, United States

Abstract

The demand of an accurate objective image quality assessment tool is important in modern multimedia systems. Image coding algorithms introduce highly structured coding artifacts and distortions. In this paper, we present a novel approach to predict the perceived image quality. Properties of the Human Visual System (HVS) were exploited to select a set of suitable metrics. These metrics are extracted while comparing the reference and distorted image. Mutual Information (MI) and Principal Component Analysis (PCA) were used to obtain an optimal set of objective features that best describe the perceived image quality in respect to subjective scores from human observers. The impairment feature vector is forwarded to the Artificial Neural Network (ANN) where the features are combined and the predicted quality score is computed. Parameters of the ANN are adjusted using Mean Opinion Scores (MOS) obtained from the group of assessors. It is shown that the proposed image quality assessment model can achieve high correlation with the subjective image quality ratings. Possible incorporation of the model into a perceptual image-coding algorithm is proposed. Such a system is capable to ensure that only visually important information is encoded and consequently that the required communication bandwidth is minimized.

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Karel Fliegel "A model utilizing artificial neural network for perceptual image quality assessment in image compression algorithms", Proc. SPIE 6315, Mathematics of Data/Image Pattern Recognition, Compression, and Encryption with Applications IX, 631507 (25 August 2006); https://doi.org/10.1117/12.679564

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KEYWORDS

Image quality

Image compression

Molybdenum

Principal component analysis

Artificial neural networks

Statistical modeling

Data modeling

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