Color image quality assessment with biologically inspired feature and machine learning In this paper, we present a new noreference 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 Cl 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.

Limitation and Challenges of Image Quality Measurement Subjectivelyrated image databases have become increasingly popular in the evaluation of image quality measurement algorithms. Several groups recently have improved their metricsu0027 performance in matching these databases, using particular HVS (human visual system) properties or image statistical models. However, it is difficult to know whether these improvements are due to progress towards mimicking the perceptual properties, or are due to matching some characteristics of the databases. This paper demonstrates an inherent limitation in using such databases, showing that our very simple metric, built on the contrast masking effect, is able to perform as good as many stateoftheart metrics. It is also argued that existent databases neither contain enough images with particularly biased distortions to test the significance of single HVS property, nor cover diverse distortion types to reflect the requirement of emerging applications.