Visual Quality Assessment and Blur Detection Based on the Transform of Gradient Magnitudes

Digital imaging and image processing technologies have revolutionized the way in which

we capture, store, receive, view, utilize, and share images. In image-based applications,

through different processing stages (e.g., acquisition, compression, and transmission), images

are subjected to different types of distortions which degrade their visual quality. Image

Quality Assessment (IQA) attempts to use computational models to automatically evaluate

and estimate the image quality in accordance with subjective evaluations. Moreover, with

the fast development of computer vision techniques, it is important in practice to extract

and understand the information contained in blurred images or regions.

The work in this dissertation focuses on reduced-reference visual quality assessment of

images and textures, as well as perceptual-based spatially-varying blur detection.

A training-free low-cost Reduced-Reference IQA (RRIQA) method is proposed. The

proposed method requires a very small number of reduced-reference (RR) features. Extensive

experiments performed on different benchmark databases demonstrate that the proposed

RRIQA method, delivers highly competitive performance as compared with the

state-of-the-art RRIQA models for both natural and texture images.

In the context of texture, the effect of texture granularity on the quality of synthesized

textures is studied. Moreover, two RR objective visual quality assessment methods that

quantify the perceived quality of synthesized textures are proposed. Performance evaluations

on two synthesized texture databases demonstrate that the proposed RR metrics outperforms

full-reference (FR), no-reference (NR), and RR state-of-the-art quality metrics in

predicting the perceived visual quality of the synthesized textures.

Last but not least, an effective approach to address the spatially-varying blur detection

problem from a single image without requiring any knowledge about the blur type, level,

or camera settings is proposed. The evaluations of the proposed approach on a diverse

sets of blurry images with different blur types, levels, and content demonstrate that the

proposed algorithm performs favorably against the state-of-the-art methods qualitatively

and quantitatively.