Statistical Models Of Natural Images Research Articles

Closed-Form Correlation Model of Oriented Bandpass Natural Images

Most prevalent statistical models of natural images characterize only the univariate distributions of divisively normalized bandpass responses or wavelet-like decompositions of them. However, the higher-order dependencies between spatially neighboring responses are not yet well understood. Towards filling this gap, we propose a new closed-form spatial-oriented correlation model that captures statistical regularities between perceptually decomposed natural image luminance samples. We validate the new correlation model on a variety of natural images. Experimental results demonstrate the robustness of the new correlation model across image content. A software release that implements the new closed-form spatial-oriented correlation model is available at http://live.ece.utexas.edu/research/3dnss/bicorr_release.zip.

In All Likelihood, Deep Belief Is Not Enough

Statistical models of natural images provide an important tool for researchers in the fields of machine learning and computational neuroscience. The canonical measure to quantitatively assess and c...

Sparse correlation coefficient for objective image quality assessment

Image quality assessment (IQA) is of fundamental importance to numerous image processing applications. Generally, image quality metrics (IQMs) regard image quality as fidelity or similarity with a reference image in some perceptual space. Such a full-reference IQA method is a kind of comparison that involves measuring the similarity or difference between two signals in a perceptually meaningful way. Modeling of the human visual system (HVS) has been regarded as the most suitable way to achieve perceptual quality predictions. In fact, natural image statistics can be an effective approach to simulate the HVS, since statistical models of natural images reveal some important response properties of the HVS. A useful statistical model of natural images is sparse coding, which is equivalent to independent component analysis (ICA). It provides a very good description of the receptive fields of simple cells in the primary visual cortex. Therefore, such a statistical model can be used to simulate the visual processing at the level of the visual cortex when designing IQMs. In this paper, we propose a fidelity criterion for IQA that relates image quality with the correlation between a reference and a distorted image in the form of sparse code. The proposed visual signal fidelity metric, which is called sparse correlation coefficient (SCC), is motivated by the need to capture the correlation between two sets of outputs from a sparse model of simple cell receptive fields. The SCC represents the correlation between two visual signals of images in a cortical visual space. The experimental results after both polynomial and logistic regression demonstrate that SCC is superior to recent state-of-the-art IQMs both in single-distortion and cross-distortion tests.

Information Content Weighting for Perceptual Image Quality Assessment

Many state-of-the-art perceptual image quality assessment (IQA) algorithms share a common two-stage structure: local quality/distortion measurement followed by pooling. While significant progress has been made in measuring local image quality/distortion, the pooling stage is often done in ad-hoc ways, lacking theoretical principles and reliable computational models. This paper aims to test the hypothesis that when viewing natural images, the optimal perceptual weights for pooling should be proportional to local information content, which can be estimated in units of bit using advanced statistical models of natural images. Our extensive studies based upon six publicly-available subject-rated image databases concluded with three useful findings. First, information content weighting leads to consistent improvement in the performance of IQA algorithms. Second, surprisingly, with information content weighting, even the widely criticized peak signal-to-noise-ratio can be converted to a competitive perceptual quality measure when compared with state-of-the-art algorithms. Third, the best overall performance is achieved by combining information content weighting with multiscale structural similarity measures.

Detection of Image Compositing Based on a Statistical Model for Natural Images

摘要: Nowadays, digital images can be easily tampered due to the availability of powerful image processing software. As digital cameras continue to replace their analog counterparts, the importance of authenticating digital images, identifying their sources, and detecting forgeries is increasing. Blind image forensics is used to analyze an image in the complete absence of any digital watermark or signature. Image compositing is the most common form of digital tampering. Assuming that image compositing operations affect the inherent statistics of the image, we propose an image compositing detection method on based on a statistical model for natural image in the wavelet transform domain. The generalized Gaussian model (GGD) is employed to describe the marginal distribution of wavelet coefficients of images, and the parameters of GGD are obtained using maximum-likelihood estimator. The statistical features include GGD parameters, prediction error, mean, variance, skewness, and kurtosis at each wavelet detail subband. Then, these feature vectors are used to discriminate between natural images and composite images using support vector machine (SVM). To evaluate the performance of our proposed method, we carried out tests on the Columbia Uncompressed Image Splicing Detection Dataset and another advanced dataset, and achieved a detection accuracy of 92% and 79%, respectively. The detection performance of our method is better than that of the method using camera response function on the same dataset. 关键词: Image compositing / generalized Gaussian model (GGD) / maximum-likelihood (ML) / support vector machine (SVM) / image forensics

Universal Steganalysis Using Feature Selection Strategy for Higher Order Image Statistics

The purpose of image steganalysis is to detect the presence of hidden message in cover photographic images. Supervised learning is an effective and commonly used method to cope with difficulties of unknown image statistics and unknown steganography. Present paper proposes; a universal approach for steganalysis for detecting presence of hidden messages embedded within digital images. This paper describes wavelet like decomposition to build higher order statistical model of natural images. Feature selection technique like ANOVA is used to select relevant features. SVM are then used to discriminate between clean and stego images. Study of the effect of relevant features on classification accuracy may help to improve the complexity. The full text of the article is not available in the cache. Kindly refer the IJCA digital library at www.ijcaonline.org for the complete article. In case, you face problems while downloading the full-text, please send a mail to editor at editor@ijcaonline.org

Detection of Image Compositing Based on a Statistical Model for Natural Images

Nowadays, digital images can be easily tampered due to the availability of powerful image processing software. As digital cameras continue to replace their analog counterparts, the importance of authenticating digital images, identifying their sources, and detecting forgeries is increasing. Blind image forensics is used to analyze an image in the complete absence of any digital watermark or signature. Image compositing is the most common form of digital tampering. Assuming that image compositing operations affect the inherent statistics of the image, we propose an image compositing detection method on based on a statistical model for natural image in the wavelet transform domain. The generalized Gaussian model (GGD) is employed to describe the marginal distribution of wavelet coefficients of images, and the parameters of GGD are obtained using maximum-likelihood estimator. The statistical features include GGD parameters, prediction error, mean, variance, skewness, and kurtosis at each wavelet detail subband. Then, these feature vectors are used to discriminate between natural images and composite images using support vector machine (SVM). To evaluate the performance of our proposed method, we carried out tests on the Columbia Uncompressed Image Splicing Detection Dataset and another advanced dataset, and achieved a detection accuracy of 92 % and 79 %, respectively. The detection performance of our method is better than that of the method using camera response function on the same dataset.

Statistical models of natural images and cortical visual representation.

A fundamental question in visual neuroscience is: Why are the response properties of visual neurons as they are? A modern approach to this problem emphasizes the importance of adaptation to ecologically valid input, and it proceeds by modeling statistical regularities in ecologically valid visual input (natural images). A seminal model was linear sparse coding, which is equivalent to independent component analysis (ICA), and provided a very good description of the receptive fields of simple cells. Further models based on modeling residual dependencies of the ''independent" components have later been introduced. These models lead to emergence of further properties of visual neurons: the complex cell receptive fields, the spatial organization of the cells, and some surround suppression and Gestalt effects. So far, these models have concentrated on the response properties of neurons, but they hold great potential to model various forms of inference and learning.

Complex cell pooling and the statistics of natural images

In previous work, we presented a statistical model of natural images that produced outputs similar to receptive fields of complex cells in primary visual cortex. However, a weakness of that model was that the structure of the pooling was assumed a priori and not learned from the statistical properties of natural images. Here, we present an extended model in which the pooling nonlinearity and the size of the subspaces are optimized rather than fixed, so we make much fewer assumptions about the pooling. Results on natural images indicate that the best probabilistic representation is formed when the size of the subspaces is relatively large, and that the likelihood is considerably higher than for a simple linear model with no pooling. Further, we show that the optimal nonlinearity for the pooling is squaring. We also highlight the importance of contrast gain control for the performance of the model. Our model is novel in that it is the first to analyze optimal subspace size and how this size is influenced by contrast normalization.

Quality-aware images

We propose the concept of quality-aware image, in which certain extracted features of the original (high-quality) image are embedded into the image data as invisible hidden messages. When a distorted version of such an image is received, users can decode the hidden messages and use them to provide an objective measure of the quality of the distorted image. To demonstrate the idea, we build a practical quality-aware image encoding, decoding and quality analysis system, which employs: 1) a novel reduced-reference image quality assessment algorithm based on a statistical model of natural images and 2) a previously developed quantization watermarking-based data hiding technique in the wavelet transform domain.

Simple-Cell-Like Receptive Fields Maximize Temporal Coherence in Natural Video

Recently, statistical models of natural images have shown the emergence of several properties of the visual cortex. Most models have considered the nongaussian properties of static image patches, leading to sparse coding or independent component analysis. Here we consider the basic time dependencies of image sequences instead of their nongaussianity. We show that simple-cell-type receptive fields emerge when temporal response strength correlation is maximized for natural image sequences. Thus, temporal response strength correlation, which is a nonlinear measure of temporal coherence, provides an alternative to sparseness in modeling simple-cell receptive field properties. Our results also suggest an interpretation of simple cells in terms of invariant coding principles, which have previously been used to explain complex-cell receptive fields.