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

abstract: 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.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Image Quality Evaluation in Lossy Compressed Images

This research focuses on the quantification of image quality in lossy compressed images, exploring the impact of digital artefacts and scene characteristics upon image quality evaluation. A subjective paired comparison test was implemented to assess perceived quality of JPEG 2000 against baseline JPEG over a range of different scene types. Interval scales were generated for both algorithms, which indicated a subjective preference for JPEG 2000, particularly at low bit rates, and these were confirmed by an objective distortion measure. The subjective results did not follow this trend for some scenes however, and both algorithms were found to be scene dependent as a result of the artefacts produced at high compression rates. The scene dependencies were explored from the interval scale results, which allowed scenes to be grouped according to their susceptibilities to each of the algorithms. Groupings were correlated with scene measures applied in a linked study. A pilot study was undertaken to explore perceptibility thresholds of JPEG 2000 of the same set of images. This work was developed with a further experiment to investigate the thresholds of perceptibility and acceptability of higher resolution JPEG 2000 compressed images. A set of images was captured using a professional level full-frame Digital Single Lens Reflex camera, using a raw workflow and carefully controlled image-processing pipeline. The scenes were quantified using a set of simple scene metrics to classify them according to whether they were average, higher than, or lower than average, for a number of scene properties known to affect image compression and perceived image quality; these were used to make a final selection of test images. Image fidelity was investigated using the method of constant stimuli to quantify perceptibility thresholds and just noticeable differences (JNDs) of perceptibility. Thresholds and JNDs of acceptability were also quantified to explore suprathreshold quality evaluation. The relationships between the two thresholds were examined and correlated with the results from the scene measures, to identify more or less susceptible scenes. It was found that the level and differences between the two thresholds was an indicator of scene dependency and could be predicted by certain types of scene characteristics. A third study implemented the soft copy quality ruler as an alternative psychophysical method, by matching the quality of compressed images to a set of images varying in a single attribute, separated by known JND increments of quality. The imaging chain and image processing workflow were evaluated using objective measures of tone reproduction and spatial frequency response. An alternative approach to the creation of ruler images was implemented and tested, and the resulting quality rulers were used to evaluate a subset of the images from the previous study. The quality ruler was found to be successful in identifying scene susceptibilities and observer sensitivity. The fourth investigation explored the implementation of four different image quality metrics. These were the Modular Image Difference Metric, the Structural Similarity Metric, The Multi-scale Structural Similarity Metric and the Weighted Structural Similarity Metric. The metrics were tested against the subjective results and all were found to have linear correlation in terms of predictability of image quality

Adaptive Nonlocal Signal Restoration and Enhancement Techniques for High-Dimensional Data

The large number of practical applications involving digital images has motivated a significant interest towards restoration solutions that improve the visual quality of the data under the presence of various acquisition and compression artifacts. Digital images are the results of an acquisition process based on the measurement of a physical quantity of interest incident upon an imaging sensor over a specified period of time. The quantity of interest depends on the targeted imaging application. Common imaging sensors measure the number of photons impinging over a dense grid of photodetectors in order to produce an image similar to what is perceived by the human visual system. Different applications focus on the part of the electromagnetic spectrum not visible by the human visual system, and thus require different sensing technologies to form the image. In all cases, even with the advance of technology, raw data is invariably affected by a variety of inherent and external disturbing factors, such as the stochastic nature of the measurement processes or challenging sensing conditions, which may cause, e.g., noise, blur, geometrical distortion and color aberration. In this thesis we introduce two filtering frameworks for video and volumetric data restoration based on the BM3D grouping and collaborative filtering paradigm. In its general form, the BM3D paradigm leverages the correlation present within a nonlocal emph{group} composed of mutually similar basic filtering elements, e.g., patches, to attain an enhanced sparse representation of the group in a suitable transform domain where the energy of the meaningful part of the signal can be thus separated from that of the noise through coefficient shrinkage. We argue that the success of this approach largely depends on the form of the used basic filtering elements, which in turn define the subsequent spectral representation of the nonlocal group. Thus, the main contribution of this thesis consists in tailoring specific basic filtering elements to the the inherent characteristics of the processed data at hand. Specifically, we embed the local spatial correlation present in volumetric data through 3-D cubes, and the local spatial and temporal correlation present in videos through 3-D spatiotemporal volumes, i.e. sequences of 2-D blocks following a motion trajectory. The foundational aspect of this work is the analysis of the particular spectral representation of these elements. Specifically, our frameworks stack mutually similar 3-D patches along an additional fourth dimension, thus forming a 4-D data structure. By doing so, an effective group spectral description can be formed, as the phenomena acting along different dimensions in the data can be precisely localized along different spectral hyperplanes, and thus different filtering shrinkage strategies can be applied to different spectral coefficients to achieve the desired filtering results. This constitutes a decisive difference with the shrinkage traditionally employed in BM3D-algorithms, where different hyperplanes of the group spectrum are shrunk subject to the same degradation model. Different image processing problems rely on different observation models and typically require specific algorithms to filter the corrupted data. As a consequent contribution of this thesis, we show that our high-dimensional filtering model allows to target heterogeneous noise models, e.g., characterized by spatial and temporal correlation, signal-dependent distributions, spatially varying statistics, and non-white power spectral densities, without essential modifications to the algorithm structure. As a result, we develop state-of-the-art methods for a variety of fundamental image processing problems, such as denoising, deblocking, enhancement, deflickering, and reconstruction, which also find practical applications in consumer, medical, and thermal imaging