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

Texture Structure Analysis

abstract: Texture analysis plays an important role in applications like automated pattern inspection, image and video compression, content-based image retrieval, remote-sensing, medical imaging and document processing, to name a few. Texture Structure Analysis is the process of studying the structure present in the textures. This structure can be expressed in terms of perceived regularity. Our human visual system (HVS) uses the perceived regularity as one of the important pre-attentive cues in low-level image understanding. Similar to the HVS, image processing and computer vision systems can make fast and efficient decisions if they can quantify this regularity automatically. In this work, the problem of quantifying the degree of perceived regularity when looking at an arbitrary texture is introduced and addressed. One key contribution of this work is in proposing an objective no-reference perceptual texture regularity metric based on visual saliency. Other key contributions include an adaptive texture synthesis method based on texture regularity, and a low-complexity reduced-reference visual quality metric for assessing the quality of synthesized textures. In order to use the best performing visual attention model on textures, the performance of the most popular visual attention models to predict the visual saliency on textures is evaluated. Since there is no publicly available database with ground-truth saliency maps on images with exclusive texture content, a new eye-tracking database is systematically built. Using the Visual Saliency Map (VSM) generated by the best visual attention model, the proposed texture regularity metric is computed. The proposed metric is based on the observation that VSM characteristics differ between textures of differing regularity. The proposed texture regularity metric is based on two texture regularity scores, namely a textural similarity score and a spatial distribution score. In order to evaluate the performance of the proposed regularity metric, a texture regularity database called RegTEX, is built as a part of this work. It is shown through subjective testing that the proposed metric has a strong correlation with the Mean Opinion Score (MOS) for the perceived regularity of textures. The proposed method is also shown to be robust to geometric and photometric transformations and outperforms some of the popular texture regularity metrics in predicting the perceived regularity. The impact of the proposed metric to improve the performance of many image-processing applications is also presented. The influence of the perceived texture regularity on the perceptual quality of synthesized textures is demonstrated through building a synthesized textures database named SynTEX. It is shown through subjective testing that textures with different degrees of perceived regularities exhibit different degrees of vulnerability to artifacts resulting from different texture synthesis approaches. This work also proposes an algorithm for adaptively selecting the appropriate texture synthesis method based on the perceived regularity of the original texture. A reduced-reference texture quality metric for texture synthesis is also proposed as part of this work. The metric is based on the change in perceived regularity and the change in perceived granularity between the original and the synthesized textures. The perceived granularity is quantified through a new granularity metric that is proposed in this work. It is shown through subjective testing that the proposed quality metric, using just 2 parameters, has a strong correlation with the MOS for the fidelity of synthesized textures and outperforms the state-of-the-art full-reference quality metrics on 3 different texture databases. Finally, the ability of the proposed regularity metric in predicting the perceived degradation of textures due to compression and blur artifacts is also established.Dissertation/ThesisPh.D. Electrical Engineering 201

Study and Comparison of Surface Roughness Measurements

National audienceThis survey paper focus on recent researches whose goal is to optimize treatments on 3D meshes, thanks to a study of their surface features, and more precisely their roughness and saliency. Applications like watermarking or lossy compression can benefit from a precise roughness detection, to better hide the watermarks or quantize coarsely these areas, without altering visually the shape. Despite investigations on scale dependence leading to multi-scale approaches, an accurate roughness or pattern characterization is still lacking, but challenging for those treatments. We think there is still room for investigations that could benefit from the power of the wavelet analysis or the fractal models. Furthermore only few works are now able to differentiate roughness from saliency, though it is essential for faithfully simplifying or denoising a 3D mesh. Hence we have investigated roughness quantification methods for analog surfaces, in several domains of physics. Some roughness parameters used in these fields and the additionnal information they bring are finally studied, since we think an adaptation for 3D meshes could be beneficial

Automatic multi-resolution spatio-frequency mottle metric (sfmm) for evaluation of macrouniformity

Evaluation of mottle is an area of on-going research in print quality assessment. We propose an unsupervised evaluation technique and a metric that measures mottle in a hard-copy laser print. The proposed algorithm uses a scanned image to quantify the low frequency variation or mottle in what is supposed to be a uniform field. `Banding\u27 and `Streaking\u27 effects are explicitly ignored and the proposed algorithm scales the test targets from Flat print (Good) to Noisy print (Bad) based on mottle only. The evaluation procedure is modeled as feature computation in different combinations of spatial, frequency and wavelet domains. The model is primarily independent of the nature of the input test target, i.e. whether it is chromatic or achromatic. The algorithm adapts accordingly and provides a mottle metric for any test target. The evaluation process is done using three major modules: (1) Pre-processing Stage, which includes acquisition of the test target and preparing it for processing; (2) Spatio-frequency Parameter Estimation where different features characterizing mottle are calculated in spatial and frequency domains; (3) Invalid Feature Removal Stage, where the invalid or insignificant features (in context to mottle) are eliminated and the dataset is ranked relatively. The algorithm was demonstrated successfully on a collection of 60 K-Only printed images spread over 2 datasets printed on 3 different faulty printers and 4 different media Also, it was tested on 5 color targets for the color version of the algorithm printed using 2 different printers and 5 different media, provided by Hewlett Packard Company

Influence of affective image content on subjective quality assessment

Image quality assessment (IQA) enables distortions introduced into an image (e.g., through lossy compression or broadcast) to be measured and evaluated for severity. It is unclear to what degree affective image content may influence this process. In this study, participants (n=25) were found to be unable to disentangle affective image content from objective image quality in a standard IQA procedure (single stimulus numerical categorical scale). We propose that this issue is worthy of consideration, particularly in single stimulus IQA techniques, in which a small number of handpicked images, not necessarily representative of the gamut of affect seen in true broadcasting, and unrated for affective content, serve as stimuli