No Reference Based Quality Assessment Using Feed Forward Neural Network

Computer Engineering

With the increasing demand for image-based applications, the efficient and reliable evaluation of image quality has increased in importance. Blind image quality assessment (BIQA) aims to predict perceptual image quality scores without access to reference images. State-of-the-art BIQA methods typically require subjects to score a large number of images to train a robust model. In past few years many successful algorithms for full reference quality assessment have been developed but general purpose no-reference approaches still lags as most of the blind approaches are distortion specific this means they could only remove a specific type of distortion that may be blockiness, blur or ringing. This limits their application domain. To overcome this limitation a new model for no-reference image quality assessment based on feed forward neural network is discussed.

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