Color image quality assessment with biologically inspired feature and machine learning

Cheng Deng; Dacheng Tao

doi:10.1117/12.863497

4 August 2010 Color image quality assessment with biologically inspired feature and machine learning

Cheng Deng, Dacheng Tao

Proceedings Volume 7744, Visual Communications and Image Processing 2010; 77440Y (2010) https://doi.org/10.1117/12.863497
Event: Visual Communications and Image Processing 2010, 2010, Huangshan, China

Abstract

In this paper, we present a new no-reference quality assessment metric for color images by using biologically inspired features (BIFs) and machine learning. In this metric, we first adopt a biologically inspired model to mimic the visual cortex and represent a color image based on BIFs which unifies color units, intensity units and C1 units. Then, in order to reduce the complexity and benefit the classification, the high dimensional features are projected to a low dimensional representation with manifold learning. Finally, a multiclass classification process is performed on this new low dimensional representation of the image and the quality assessment is based on the learned classification result in order to respect the one of the human observers. Instead of computing a final note, our method classifies the quality according to the quality scale recommended by the ITU. The preliminary results show that the developed metric can achieve good quality evaluation performance.

Citation Download Citation

Cheng Deng and Dacheng Tao "Color image quality assessment with biologically inspired feature and machine learning", Proc. SPIE 7744, Visual Communications and Image Processing 2010, 77440Y (4 August 2010); https://doi.org/10.1117/12.863497

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