Stereoscopic video quality assessment using binocular energy

Stereoscopic imaging is becoming increasingly popular. However, to ensure the best quality of experience, there is a need to develop more robust and accurate objective metrics for stereoscopic content quality assessment. Existing stereoscopic image and video metrics are either extensions of conventional 2D metrics (with added depth or disparity information) or are based on relatively simple perceptual models. Consequently, they tend to lack the accuracy and robustness required for stereoscopic content quality assessment. This paper introduces full-reference stereoscopic image and video quality metrics based on a Human Visual System (HVS) model incorporating important physiological findings on binocular vision. The proposed approach is based on the following three contributions. First, it introduces a novel HVS model extending previous models to include the phenomena of binocular suppression and recurrent excitation. Second, an image quality metric based on the novel HVS model is proposed. Finally, an optimised temporal pooling strategy is introduced to extend the metric to the video domain. Both image and video quality metrics are obtained via a training procedure to establish a relationship between subjective scores and objective measures of the HVS model. The metrics are evaluated using publicly available stereoscopic image/video databases as well as a new stereoscopic video database. An extensive experimental evaluation demonstrates the robustness of the proposed quality metrics. This indicates a considerable improvement with respect to the state-of-the-art with average correlations with subjective scores of 0.86 for the proposed stereoscopic image metric and 0.89 and 0.91 for the proposed stereoscopic video metrics

Optical Coherence Tomography Noise Reduction Using Anisotropic Local Bivariate Gaussian Mixture Prior in 3D Complex Wavelet Domain

In this paper, MMSE estimator is employed for noise-free 3D OCT data recovery in 3D complex wavelet domain. Since the proposed distribution for noise-free data plays a key role in the performance of MMSE estimator, a priori distribution for the pdf of noise-free 3D complex wavelet coefficients is proposed which is able to model the main statistical properties of wavelets. We model the coefficients with a mixture of two bivariate Gaussian pdfs with local parameters which are able to capture the heavy-tailed property and inter- and intrascale dependencies of coefficients. In addition, based on the special structure of OCT images, we use an anisotropic windowing procedure for local parameters estimation that results in visual quality improvement. On this base, several OCT despeckling algorithms are obtained based on using Gaussian/two-sided Rayleigh noise distribution and homomorphic/nonhomomorphic model. In order to evaluate the performance of the proposed algorithm, we use 156 selected ROIs from 650 × 512 × 128 OCT dataset in the presence of wet AMD pathology. Our simulations show that the best MMSE estimator using local bivariate mixture prior is for the nonhomomorphic model in the presence of Gaussian noise which results in an improvement of 7.8 ± 1.7 in CNR

Contourlet Domain Image Modeling and its Applications in Watermarking and Denoising

Statistical image modeling in sparse domain has recently attracted a great deal of research interest. Contourlet transform as a two-dimensional transform with multiscale and multi-directional properties is known to effectively capture the smooth contours and geometrical structures in images. The objective of this thesis is to study the statistical properties of the contourlet coefficients of images and develop statistically-based image denoising and watermarking schemes. Through an experimental investigation, it is first established that the distributions of the contourlet subband coefficients of natural images are significantly non-Gaussian with heavy-tails and they can be best described by the heavy-tailed statistical distributions, such as the alpha-stable family of distributions. It is shown that the univariate members of this family are capable of accurately fitting the marginal distributions of the empirical data and that the bivariate members can accurately characterize the inter-scale dependencies of the contourlet coefficients of an image. Based on the modeling results, a new method in image denoising in the contourlet domain is proposed. The Bayesian maximum a posteriori and minimum mean absolute error estimators are developed to determine the noise-free contourlet coefficients of grayscale and color images. Extensive experiments are conducted using a wide variety of images from a number of databases to evaluate the performance of the proposed image denoising scheme and to compare it with that of other existing schemes. It is shown that the proposed denoising scheme based on the alpha-stable distributions outperforms these other methods in terms of the peak signal-to-noise ratio and mean structural similarity index, as well as in terms of visual quality of the denoised images. The alpha-stable model is also used in developing new multiplicative watermark schemes for grayscale and color images. Closed-form expressions are derived for the log-likelihood-based multiplicative watermark detection algorithm for grayscale images using the univariate and bivariate Cauchy members of the alpha-stable family. A multiplicative multichannel watermark detector is also designed for color images using the multivariate Cauchy distribution. Simulation results demonstrate not only the effectiveness of the proposed image watermarking schemes in terms of the invisibility of the watermark, but also the superiority of the watermark detectors in providing detection rates higher than that of the state-of-the-art schemes even for the watermarked images undergone various kinds of attacks

Video object extraction in distributed surveillance systems

Recently, automated video surveillance and related video processing algorithms have received considerable attention from the research community. Challenges in video surveillance rise from noise, illumination changes, camera motion, splits and occlusions, complex human behavior, and how to manage extracted surveillance information for delivery, archiving, and retrieval: Many video surveillance systems focus on video object extraction, while few focus on both the system architecture and video object extraction. We focus on both and integrate them to produce an end-to-end system and study the challenges associated with building this system. We propose a scalable, distributed, and real-time video-surveillance system with a novel architecture, indexing, and retrieval. The system consists of three modules: video workstations for processing, control workstations for monitoring, and a server for management and archiving. The proposed system models object features as temporal Gaussians and produces: an 18 frames/second frame-rate for SIF video and static cameras, reduced network and storage usage, and precise retrieval results. It is more scalable and delivers more balanced distributed performance than recent architectures. The first stage of video processing is noise estimation. We propose a method for localizing homogeneity and estimating the additive white Gaussian noise variance, which uses spatially scattered initial seeds and utilizes particle filtering techniques to guide their spatial movement towards homogeneous locations from which the estimation is performed. The noise estimation method reduces the number of measurements required by block-based methods while achieving more accuracy. Next, we segment video objects using a background subtraction technique. We generate the background model online for static cameras using a mixture of Gaussians background maintenance approach. For moving cameras, we use a global motion estimation method offline to bring neighboring frames into the coordinate system of the current frame and we merge them to produce the background model. We track detected objects using a feature-based object tracking method with improved detection and correction of occlusion and split. We detect occlusion and split through the identification of sudden variations in the spatia-temporal features of objects. To detect splits, we analyze the temporal behavior of split objects to discriminate between errors in segmentation and real separation of objects. Both objective and subjective experimental results show the ability of the proposed algorithm to detect and correct both splits and occlusions of objects. For the last stage of video processing, we propose a novel method for the detection of vandalism events which is based on a proposed definition for vandal behaviors recorded on surveillance video sequences. We monitor changes inside a restricted site containing vandalism-prone objects and declare vandalism when an object is detected as leaving the site while there is temporally consistent and significant static changes representing damage, given that the site is normally unchanged after use. The proposed method is tested on sequences showing real and simulated vandal behaviors and it achieves a detection rate of 96%. It detects different forms of vandalism such as graffiti and theft. The proposed end-ta-end video surveillance system aims at realizing the potential of video object extraction in automated surveillance and retrieval by focusing on both video object extraction and the management, delivery, and utilization of the extracted informatio

AUTOMATED ESTIMATION, REDUCTION, AND QUALITY ASSESSMENT OF VIDEO NOISE FROM DIFFERENT SOURCES

Estimating and removing noise from video signals is important to increase either the visual quality of video signals or the performance of video processing algorithms such as compression or segmentation where noise estimation or reduction is a pre-processing step. To estimate and remove noise, effective methods use both spatial and temporal information to increase the reliability of signal extraction from noise. The objective of this thesis is to introduce a video system having three novel techniques to estimate and reduce video noise from different sources, both effectively and efficiently and assess video quality without considering a reference non-noisy video. The first (intensity-variances based homogeneity classification) technique estimates visual noise of different types in images and video signals. The noise can be white Gaussian noise, mixed Poissonian- Gaussian (signal-dependent white) noise, or processed (frequency-dependent) noise. The method is based on the classification of intensity-variances of signal patches in order to find homogeneous regions that best represent the noise signal in the input signal. The method assumes that noise is signal-independent in each intensity class. To find homogeneous regions, the method works on the downsampled input image and divides it into patches. Each patch is assigned to an intensity class, whereas outlier patches are rejected. Then the most homogeneous cluster is selected and its noise variance is considered as the peak of noise variance. To account for processed noise, we estimate the degree of spatial correlation. To account for temporal noise variations a stabilization process is proposed. We show that the proposed method competes related state-of-the-art in noise estimation. The second technique provides solutions to remove real-world camera noise such as signal-independent, signal-dependent noise, and frequency-dependent noise. Firstly, we propose a noise equalization method in intensity and frequency domain which enables a white Gaussian noise filter to handle real noise. Our experiments confirm the quality improvement under real noise while white Gaussian noise filter is used with our equalization method. Secondly, we propose a band-limited time-space video denoiser which reduces video noise of different types. This denoiser consists of: 1) intensity-domain noise equalization to account for signal dependency, 2) band-limited anti-blocking time-domain filtering of current frame using motion-compensated previous and subsequent frames, 3) spatial filtering combined with noise frequency equalizer to remove residual noise left from temporal filtering, and 4) intensity de-equalization to invert the first step. To decrease the chance of motion blur, temporal weights are calculated using two levels of error estimation; coarse (blocklevel) and fine (pixel-level). We correct the erroneous motion vectors by creating a homography from reliable motion vectors. To eliminate blockiness in block-based temporal filter, we propose three ideas: interpolation of block-level error, a band-limited filtering by subtracting the back-signal beforehand, and two-band motion compensation. The proposed time-space filter is parallelizable to be significantly accelerated by GPU. We show that the proposed method competes related state-ofthe- art in video denoising. The third (sparsity and dominant orientation quality index) technique is a new method to assess the quality of the denoised video frames without a reference (clean frames). In many image and video applications, a quantitative measure of image content, noise, and blur is required to facilitate quality assessment, when the ground-truth is not available. We propose a fast method to find the dominant orientation of image patches, which is used to decompose them into singular values. Combining singular values with the sparsity of the patch in the transform domain, we measure the possible image content and noise of the patches and of the whole image. To measure the effect of noise accurately, our method takes both low and high textured patches into account. Before analyzing the patches, we apply a shrinkage in the transform domain to increase the contrast of genuine image structure. We show that the proposed method is useful to select parameters of denoising algorithms automatically in different noise scenarios such as white Gaussian and real noise. Our objective and subjective results confirm the correspondence between the measured quality and the ground-truth and proposed method rivals related state-of-the-art approaches