Self-Similar Anisotropic Texture Analysis: the Hyperbolic Wavelet Transform Contribution

Textures in images can often be well modeled using self-similar processes while they may at the same time display anisotropy. The present contribution thus aims at studying jointly selfsimilarity and anisotropy by focusing on a specific classical class of Gaussian anisotropic selfsimilar processes. It will first be shown that accurate joint estimates of the anisotropy and selfsimilarity parameters are performed by replacing the standard 2D-discrete wavelet transform by the hyperbolic wavelet transform, which permits the use of different dilation factors along the horizontal and vertical axis. Defining anisotropy requires a reference direction that needs not a priori match the horizontal and vertical axes according to which the images are digitized, this discrepancy defines a rotation angle. Second, we show that this rotation angle can be jointly estimated. Third, a non parametric bootstrap based procedure is described, that provides confidence interval in addition to the estimates themselves and enables to construct an isotropy test procedure, that can be applied to a single texture image. Fourth, the robustness and versatility of the proposed analysis is illustrated by being applied to a large variety of different isotropic and anisotropic self-similar fields. As an illustration, we show that a true anisotropy built-in self-similarity can be disentangled from an isotropic self-similarity to which an anisotropic trend has been superimposed

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

Combining local regularity estimation and total variation optimization for scale-free texture segmentation

Texture segmentation constitutes a standard image processing task, crucial to many applications. The present contribution focuses on the particular subset of scale-free textures and its originality resides in the combination of three key ingredients: First, texture characterization relies on the concept of local regularity ; Second, estimation of local regularity is based on new multiscale quantities referred to as wavelet leaders ; Third, segmentation from local regularity faces a fundamental bias variance trade-off: In nature, local regularity estimation shows high variability that impairs the detection of changes, while a posteriori smoothing of regularity estimates precludes from locating correctly changes. Instead, the present contribution proposes several variational problem formulations based on total variation and proximal resolutions that effectively circumvent this trade-off. Estimation and segmentation performance for the proposed procedures are quantified and compared on synthetic as well as on real-world textures

Edge-guided image gap interpolation using multi-scale transformation

This paper presents improvements in image gap restoration through the incorporation of edge-based directional interpolation within multi-scale pyramid transforms. Two types of image edges are reconstructed: 1) the local edges or textures, inferred from the gradients of the neighboring pixels and 2) the global edges between image objects or segments, inferred using a Canny detector. Through a process of pyramid transformation and downsampling, the image is progressively transformed into a series of reduced size layers until at the pyramid apex the gap size is one sample. At each layer, an edge skeleton image is extracted for edge-guided interpolation. The process is then reversed; from the apex, at each layer, the missing samples are estimated (an iterative method is used in the last stage of upsampling), up-sampled, and combined with the available samples of the next layer. Discrete cosine transform and a family of discrete wavelet transforms are utilized as alternatives for pyramid construction. Evaluations over a range of images, in regular and random loss pattern, at loss rates of up to 40%, demonstrate that the proposed method improves peak-signal-to-noise-ratio by 1–5 dB compared with a range of best-published works

Modeling of 2D+1 texture movies for video coding

We propose a novel model-based coding system for video. Model-based coding aims at improving compression gain by replacing the non-informative image elements with some perceptually equivalent models. Images enclosing large textured regions are ideal candidates. Texture movies are obtained by filming a static texture with a moving camera. The integration of the motion information within the generative texture process allows to replace the “real” texture with a “visually equivalent” synthetic one, while preserving the correct motion perception. Global motion estimation is used to determine the movement of the camera and to identify the overlapping region between two successive frames. Such an information is then exploited for the generation of the texture movies. The proposed method for synthesizing 2D+1 texture movies is able to emulate any piece-wise linear trajectory. Compression performances are very encouraging. On this kind of video sequences, the proposed method improves the compression rate of an MPEG4 state-of-the-art video coder of an order of magnitude while providing a sensibly better perceptual quality. Importantly, the current implementation is real-time on Intel PIII processors