International Conference on Computer and Knowledge Engineering

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Blind image quality assessment based on Multi-resolution Local Structures

Authors :

Seyed Majid Khorashadizadeh¹ Mehdi Sadeghi Bakhi² Fatemeh Seifishahpar³ AliMohammad Latif⁴

1- Computer Engineering Department, Yazd University, Yazd, Iran 2- Ferdowsi University of Mashhad 3- Department of Electrical and Computer Engineering, University of Victoria 4- Computer Engineering Department, Yazd University, Yazd, Iran

Keywords :

Image quality assessment; No-reference IQA; Local Statistics; Multi-resolution

Abstract :

Our approach to no-reference image quality assessment (IQA) is grounded on the assumption that natural scenes have certain statistical properties which are altered in the presence of distortion. Therefore, by measuring this statistic one can recognize the distortion affecting the image and provide a no-reference (NR) IQA. In this paper, we extract local entropy and standard deviation from non-overlapping 8 × 8 blocks in the image and its gradient version from 2 scales. Extracting features from gradient images can help to better distinguishing blur and noise effects on images. Having extracted local measures, we aggregate them by finding the parameters of a Gaussian mixture model fitted on these local measures. Finally, our quality metric is achieved via a Neural Network machine learning tool, and its performance is evaluated on a standard LIVE database. The experimental results point out that our proposed features not only can identify the type of distortion but also are consistent with the DMOS in subjective assessment in terms of the correlation coefficient.