Natural Scene Statistics Model Research Articles

HDR-BVQM: High dynamic range blind video quality model

Considerable progress has been made toward developing standard dynamic range (SDR) blind video quality assessment (BVQA) models that do not require any baseline reference for quality prediction. However, there is no such method for the high dynamic range (HDR) content. Unlike SDR video, HDR video represents a high-fidelity representation of the real-world scene by preserving the wide luminance range and color gamut. Therefore, SDR BVQA models are not suitable for HDR BVQA. Towards ameliorating this, a first-of-its-kind BVQA model for HDR content is presented in this work. The proposed HDR blind video quality model (HDR-BVQM) is inspired by the spatio-temporal natural scene statistics model, previously employed in SDR blind quality assessment metrics. To build our proposed model, we first develop a comprehensive subjective HDR video quality dataset, including 228 distorted videos generated through three different (H.264, HEVC, packet drop) distortion processes from 19 pristine HDR videos. The developed dataset is then used to extract HDR relevant features, which vary in different distortion types, to train and test the proposed HDR-BVQM. The features are based on the pointwise, pairwise log-derivative, and motion coherence based statistics. Finally, detailed validation and performance comparison is performed with full-reference HDR and no-reference SDR quality assessment methods. The results reveal that the quality prediction by HDR-BVQM correlates with the human judgment of quality.

A photo‐based quality assessment model for the estimation of PM2.5 concentrations

Rapid economic growth has caused severe environmental pollution, which has aroused great concern. This pollution affects public health and impairs visibility, therefore, it should be given greater consideration. In this paper, a photo-based PM2.5 concentration predictor is proposed based on the natural scene statistics without artificial assistance or extra information. Given that the quality of PM2.5 concentration images is determined by many factors, three types of influencing factors are analysed: the colourfulness, the structural degradation and the contrast. The first feature consists of the hue, saturation and colour descriptors, which measure the colourfulness of the PM2.5 concentration images. The second feature is determined based on the contrast can effectively portray the quality of PM2.5 concentration in the images. The third feature is extracted based on the natural scene statistics model, which measures the local and global structural degradation information and the naturalness of the PM2.5 concentration images. Finally, the three features are used to train a random forest model that can be used to predict the concentration of PM2.5. Experimental results illustrate that the performance of the proposed model is better than those of popular competitors on AQID.

Perceptual Monocular Depth Estimation

Monocular depth estimation (MDE), which is the task of using a single image to predict scene depths, has gained considerable interest, in large part owing to the popularity of applying deep learning methods to solve “computer vision problems”. Monocular cues provide sufficient data for humans to instantaneously extract an understanding of scene geometries and relative depths, which is evidence of both the processing power of the human visual system and the predictive power of the monocular data. However, developing computational models to predict depth from monocular images remains challenging. Hand-designed MDE features do not perform particularly well, and even current “deep” models are still evolving. Here we propose a novel approach that uses perceptually-relevant natural scene statistics (NSS) features to predict depths from monocular images in a simple, scale-agnostic way that is competitive with state-of-the-art systems. While the statistics of natural photographic images have been successfully used in a variety of image and video processing, analysis, and quality assessment tasks, they have never been applied in a predictive end-to-end deep-learning model for monocular depth. Correspondingly, no previous work has explicitly incorporated perceptual features in a monocular depth-prediction approach. Here we accomplish this by developing a new closed-form bivariate model of image luminances and use features extracted from this model and from other NSS models to drive a novel deep learning framework for predicting depth given a single image.

Natural scene statistics model independent no-reference image quality assessment using patch based discrete cosine transform

Most of no-reference image quality assessment (NR-IQA) techniques reported in literature have utilized transform coefficients, which are modeled using curve fitting to extract features based on natural scene statistics (NSS). The performance of NR-IQA techniques that utilize curve-fitting suffers from degradation in performance because the distribution of curve fitted NSS features deviate from the statistical distribution of a distorted image. Although deep convolutional neural networks (DCNNs) have been used for NR-IQA that are NSS model-independent but their performance is dependent upon the size of training data. The available datasets for NR-IQA are small, therefore data augmentation is used that affects the performance of DCNN based NR-IQA techniques and is also computationally expensive. This work proposes a new patch-based NR-IQA technique, which utilizes features extracted from discrete cosine transform coefficients. The proposed technique is curve fitting independent and helps in avoiding errors in the statistical distribution of NSS features. It relies on global statistics to estimate image quality based on local patches, which allow us to decompose the statistics of images. The proposed technique divides the image into patches and extracts nine handcrafted features i.e., entropy, mean, variance, skewness, kurtosis, mobility, band power, energy, complexity, and peak to peak value. The extracted features are used with a support vector regression model to predict the image quality score. The experimental results have shown that the proposed technique is database and image content-independent. It shows better performance over a majority of distortion types and on images taken in real-time.

Cloud Detection in Satellite Images Based on Natural Scene Statistics and Gabor Features

Cloud detection is an important task in remote sensing (RS) image processing. Numerous cloud detection algorithms have been developed. However, most existing methods suffer from the weakness of omitting small and thin clouds, and from an inability to discriminate clouds from photometrically similar regions, such as buildings and snow. Here, we derive a novel cloud detection algorithm for optical RS images, whereby test images are separated into three classes: thick clouds, thin clouds, and noncloudy. First, a simple linear iterative clustering algorithm is adopted that is able to segment potential clouds, including small clouds. Then, a natural scene statistics model is applied to the superpixels to distinguish between clouds and surface buildings. Finally, Gabor features are computed within each superpixel and a support vector machine is used to distinguish clouds from snow regions. The experimental results indicate that the proposed model outperforms state-of-the-art methods for cloud detection.

Blind image quality assessment based on joint log-contrast statistics

During recent years, quality-aware features extracted from natural scene statistics (NSS) models have been used in development of blind image quality assessment (BIQA) algorithms. Generally, the univariate distributions of bandpass coefficients are used to fit a parametric probabilistic model and the model parameters serve as the quality-aware features. However, the inter-location, inter-direction and inter-scale correlations of natural images cannot be well exploited by such NSS models, as it is hard to capture such dependencies using univariate marginal distributions. In this paper, we build a novel NSS model of joint log-contrast distribution to take into account the across space and direction correlations of natural images (inter-scale correlation to be explored as the next step). Furthermore, we provide a new efficient approach to extract quality-aware features as the gradient of log-likelihood on the NSS model, instead of using model parameters directly. Finally, we develop an effective joint-NSS model based BIQA metric called BJLC (BIQA based on joint log-contrast statistics). Extensive experiments on four public large-scale image databases have validated that objective quality scores predicted by the proposed BIQA method are in higher accordance with subjective ratings generated by human observers compared with existing methods.

Towards a Closed Form Second-Order Natural Scene Statistics Model.

Previous work on natural scene statistics (NSS)-based image models has focused primarily on characterizing the univariate bandpass statistics of single pixels. These models have proven to be powerful tools driving a variety of computer vision and image/video processing applications, including depth estimation, image quality assessment, and image denoising, among others. Multivariate NSS models descriptive of the joint distributions of spatially separated bandpass image samples have, however, received relatively little attention. Here, we develop a closed form bivariate spatial correlation model of bandpass and normalized image samples that completes an existing 2D joint generalized Gaussian distribution model of adjacent bandpass pixels. Our model is built using a set of diverse, high-quality naturalistic photographs, and as a control, we study the model properties on white noise. We also study the way the model fits are affected when the images are modified by common distortions.

Full Reference Quality Assessment for Image Retargeting Based on Natural Scene Statistics Modeling and Bi-Directional Saliency Similarity

Image retargeting technology has been widely studied to adapt images for the devices with heterogeneous screen resolutions. Meanwhile effective objective retargeting quality assessment algorithms are also very important for optimizing and selecting favorable retargeting methods. Unlike previous assessment algorithms which rely on image local structure features and unidirectional prediction of information loss, we propose a bi-directional natural salient scene distortion model (BNSSD) including image natural scene statistics (NSS) measurement, salient global structure distortion measurement, and bi-directional salient information loss measurement. First, we propose a new NSS model in log-Gabor domain and verify its effectiveness in reflecting nature scene statistical distortions introduced during the retargeting process. Second, the concept of salient global structure distortion is proposed to measure the global structure uniformity in the corresponding salient regions between original and retargeted images. Finally, we propose a bidirectional salient information loss metric to measure the information loss between salient areas in original image and retargeted image. The effectiveness of the BNSSD model is verified on two widely recognized public databases, and the experimental results show that our method outperforms the state-of-the-art algorithms under different statistical assessment criteria.

Bayesian depth estimation from monocular natural images.

Estimating an accurate and naturalistic dense depth map from a single monocular photographic image is a difficult problem. Nevertheless, human observers have little difficulty understanding the depth structure implied by photographs. Two-dimensional (2D) images of the real-world environment contain significant statistical information regarding the three-dimensional (3D) structure of the world that the vision system likely exploits to compute perceived depth, monocularly as well as binocularly. Toward understanding how this might be accomplished, we propose a Bayesian model of monocular depth computation that recovers detailed 3D scene structures by extracting reliable, robust, depth-sensitive statistical features from single natural images. These features are derived using well-accepted univariate natural scene statistics (NSS) models and recent bivariate/correlation NSS models that describe the relationships between 2D photographic images and their associated depth maps. This is accomplished by building a dictionary of canonical local depth patterns from which NSS features are extracted as prior information. The dictionary is used to create a multivariate Gaussian mixture (MGM) likelihood model that associates local image features with depth patterns. A simple Bayesian predictor is then used to form spatial depth estimates. The depth results produced by the model, despite its simplicity, correlate well with ground-truth depths measured by a current-generation terrestrial light detection and ranging (LIDAR) scanner. Such a strong form of statistical depth information could be used by the visual system when creating overall estimated depth maps incorporating stereopsis, accommodation, and other conditions. Indeed, even in isolation, the Bayesian predictor delivers depth estimates that are competitive with state-of-the-art "computer vision" methods that utilize highly engineered image features and sophisticated machine learning algorithms.

No-Reference Quality Assessment of Tone-Mapped HDR Pictures.

Being able to automatically predict digital picture quality, as perceived by human observers, has become important in many applications where humans are the ultimate consumers of displayed visual information. Standard dynamic range (SDR) images provide 8 b/color/pixel. High dynamic range (HDR) images, which are usually created from multiple exposures of the same scene, can provide 16 or 32 b/color/pixel, but must be tonemapped to SDR for display on standard monitors. Multi-exposure fusion techniques bypass HDR creation, by fusing the exposure stack directly to SDR format while aiming for aesthetically pleasing luminance and color distributions. Here, we describe a new no-reference image quality assessment (NR IQA) model for HDR pictures that is based on standard measurements of the bandpass and on newly conceived differential natural scene statistics (NSS) of HDR pictures. We derive an algorithm from the model which we call the HDR IMAGE GRADient-based Evaluator. NSS models have previously been used to devise NR IQA models that effectively predict the subjective quality of SDR images, but they perform significantly worse on tonemapped HDR content. Toward ameliorating this we make here the following contributions: 1) we design HDR picture NR IQA models and algorithms using both standard space-domain NSS features as well as novel HDR-specific gradient-based features that significantly elevate prediction performance; 2) we validate the proposed models on a large-scale crowdsourced HDR image database; and 3) we demonstrate that the proposed models also perform well on legacy natural SDR images. The software is available at: http://live.ece.utexas.edu/research/Quality/higradeRelease.zip.

Blind image quality assessment with improved natural scene statistics model

No-reference (NR)/blind image quality assessment (IQA) metrics play an important role in the area of image processing. Natural scene statistics (NSS) model assumes that natural images possess certain regular statistical properties and is widely used in NR IQA metrics. Most existing NSS-based NR algorithms are achieved by measuring the variation of image statistics, which are characterized by the fitting parameters of NSS model, across different distortions. However, distortions not only change the image statistics, but also disturb the statistical regularity held by natural images. As a result, the distribution of distorted images can not well follow the NSS model. There exists fitting error between the real distribution of the distorted image and the fitted one under certain NSS model. In this paper, the statistical distributions of the distorted images are discussed in detail. We suggest to take the fitting errors into account as well as the fitting parameters for feature extraction, and propose a novel NR IQA algorithm. Experimental results on several image databases demonstrate that the proposed metric performs highly consistent with human visual perception.

Objective Quality Assessment of Interpolated Natural Images.

Image interpolation techniques that create high-resolution images from low-resolution (LR) images are widely used in real world applications, but how to evaluate the quality of interpolated images is not a well-resolved issue. Subjective assessment methods are useful and reliable, but are also slow and expensive. Here, we propose an objective method to assess the quality of an interpolated natural image using the available LR image as a reference. Our method adopts a natural scene statistics (NSS) framework, where image quality degradation is gauged by the deviation of its statistical features from the NSS models trained upon high-quality natural images. Two distortion measures are proposed, namely, interpolated natural image distortion (IND) and weighted IND. Validations by subjective tests show that the proposed approach performs statistically equivalent or sometimes better than an average human subject. Moreover, we demonstrate the potential application of the proposed method in parameter tuning of image interpolation algorithms.

Tasking on Natural Statistics of Infrared Images.

Natural scene statistics (NSSs) provide powerful, perceptually relevant tools that have been successfully used for image quality analysis of visible light images. Since NSS capture statistical regularities that arise from the physical world, they are relevant to long wave infrared (LWIR) images, which differ from visible light images mainly by the wavelengths captured at the imaging sensors. We show that NSS models of bandpass LWIR images are similar to those of visible light images, but with different parameterizations. Using this difference, we exploit the power of NSS to successfully distinguish between LWIR images and visible light images. In addition, we study distortions unique to LWIR and find directional models useful for detecting the halo effect, simple bandpass models useful for detecting hotspots, and combinations of these models useful for measuring the degree of non-uniformity present in many LWIR images. For local distortion identification and measurement, we also describe a method for generating distortion maps using NSS features. To facilitate our evaluation, we analyze the NSS of LWIR images under pristine and distorted conditions, using four databases, each captured with a different IR camera. Predicting human performance for assessing distortion and quality in LWIR images is critical for task efficacy. We find that NSS features improve human targeting task performance prediction. Furthermore, we conducted a human study on the perceptual quality of noise-and blur-distorted LWIR images and create a new blind image quality predictor for IR images.

Referenceless Prediction of Perceptual Fog Density and Perceptual Image Defogging

We propose a referenceless perceptual fog density prediction model based on natural scene statistics (NSS) and fog aware statistical features. The proposed model, called Fog Aware Density Evaluator (FADE), predicts the visibility of a foggy scene from a single image without reference to a corresponding fog-free image, without dependence on salient objects in a scene, without side geographical camera information, without estimating a depth-dependent transmission map, and without training on human-rated judgments. FADE only makes use of measurable deviations from statistical regularities observed in natural foggy and fog-free images. Fog aware statistical features that define the perceptual fog density index derive from a space domain NSS model and the observed characteristics of foggy images. FADE not only predicts perceptual fog density for the entire image, but also provides a local fog density index for each patch. The predicted fog density using FADE correlates well with human judgments of fog density taken in a subjective study on a large foggy image database. As applications, FADE not only accurately assesses the performance of defogging algorithms designed to enhance the visibility of foggy images, but also is well suited for image defogging. A new FADE-based referenceless perceptual image defogging, dubbed DEnsity of Fog Assessment-based DEfogger (DEFADE) achieves better results for darker, denser foggy images as well as on standard foggy images than the state of the art defogging methods. A software release of FADE and DEFADE is available online for public use: http://live.ece.utexas.edu/research/fog/index.html.

English

We develop an efficient general-purpose blind/no-reference image quality assessment (IQA) algorithm using a natural scene statistics (NSS) model of discrete cosine transform (DCT) coefficients. The algorithm is computationally appealing, given the availability of platforms optimized for DCT computation. The approach relies on a simple Bayesian inference model to predict image quality scores given certain extracted features. The features are based on an NSS model of the image DCT coefficients. The estimated parameters of the model are utilized to form features that are indicative of perceptual quality. These features are used in a simple Bayesian inference approach to predict quality scores. The resulting algorithm, which we name BLIINDS-II, requires minimal training and adopts a simple probabilistic model for score prediction. Given the extracted features from a test image, the quality score that maximizes the probability of the empirically determined inference model is chosen as the predicted quality score of that image. When tested on the LIVE IQA database, BLIINDS-II is shown to correlate highly with human judgments of quality, at a level that is competitive with the popular SSIM index.

Blind prediction of natural video quality.

We propose a blind (no reference or NR) video quality evaluation model that is nondistortion specific. The approach relies on a spatio-temporal model of video scenes in the discrete cosine transform domain, and on a model that characterizes the type of motion occurring in the scenes, to predict video quality. We use the models to define video statistics and perceptual features that are the basis of a video quality assessment (VQA) algorithm that does not require the presence of a pristine video to compare against in order to predict a perceptual quality score. The contributions of this paper are threefold. 1) We propose a spatio-temporal natural scene statistics (NSS) model for videos. 2) We propose a motion model that quantifies motion coherency in video scenes. 3) We show that the proposed NSS and motion coherency models are appropriate for quality assessment of videos, and we utilize them to design a blind VQA algorithm that correlates highly with human judgments of quality. The proposed algorithm, called video BLIINDS, is tested on the LIVE VQA database and on the EPFL-PoliMi video database and shown to perform close to the level of top performing reduced and full reference VQA algorithms.

No-Reference Quality Assessment of Contrast-Distorted Images Based on Natural Scene Statistics

Contrast distortion is often a determining factor in human perception of image quality, but little investigation has been dedicated to quality assessment of contrast-distorted images without assuming the availability of a perfect-quality reference image. In this letter, we propose a simple but effective method for no-reference quality assessment of contrast distorted images based on the principle of natural scene statistics (NSS). A large scale image database is employed to build NSS models based on moment and entropy features. The quality of a contrast-distorted image is then evaluated based on its unnaturalness characterized by the degree of deviation from the NSS models. Support vector regression (SVR) is employed to predict human mean opinion score (MOS) from multiple NSS features as the input. Experiments based on three publicly available databases demonstrate the promising performance of the proposed method.

Blind Image Quality Assessment: A Natural Scene Statistics Approach in the DCT Domain

We develop an efficient, general-purpose, blind/noreference image quality assessment (NR-IQA) algorithm using a natural scene statistics (NSS) model of discrete cosine transform (DCT) coefficients. The algorithm is computationally appealing, given the availability of platforms optimized for DCT computation. The approach relies on a simple Bayesian inference model to predict image quality scores given certain extracted features. The features are based on an NSS model of the image DCT coefficients. The estimated parameters of the model are utilized to form features that are indicative of perceptual quality. These features are used in a simple Bayesian inference approach to predict quality scores. The resulting algorithm, which we name BLIINDS-II, requires minimal training and adopts a simple probabilistic model for score prediction. Given the extracted features from a test image, the quality score that maximizes the probability of the empirically determined inference model is chosen as the predicted quality score of that image. When tested on the LIVE IQA database, BLIINDS-II is shown to correlate highly with human judgments of quality, at a level that is competitive with the popular SSIM index.

Nonsubsampled contourlet transform-based algorithm for no-reference image quality assessment

No-reference image quality assessment (NRIQA) is to be used when a reference image may not be available. The existing well-known NRIQA algorithms are those that use natural scene statistics (NSS), such as NSS model in wavelet domain (WNSS) and NSS model in contourlet domain (CNSS). WNSS is only applicable to evaluate JPEG2000 compressed images, while CNSS can be used to evaluate five different distortion types of image sets such as JPEG, JPEG2000, white noise, Gaussian blur, and fast fading. However, due to the downsamplers and upsamplers, the contourlet transform is not shift-invariant and may cause pseudo-Gibbs phenomena. In this paper, we propose an improved NRIQA based on the nonsubsampled contourlet transform (NSCT) which has shift-invariant characteristics, multiscale, and multidirection expansion. The basic observation of the proposed algorithm is that in the nonsubsampled contourlet domain natural images exhibit certain common joint statistical characteristics which can be represented by a mathematical model and disturbed by a wide variety of distortions. Performance evaluation tests show that the predicted quality scores obtained by the proposed algorithm are more effective and consistent with visual quality than those by WNSS or CNSS-based NRIQA on four distortion types of image sets in the LIVE image database except for JPEG2000 compressed images.

Statistical Modeling of 3-D Natural Scenes With Application to Bayesian Stereopsis

We studied the empirical distributions of luminance, range and disparity wavelet coefficients using a coregistered database of luminance and range images. The marginal distributions of range and disparity are observed to have high peaks and heavy tails, similar to the well-known properties of luminance wavelet coefficients. However, we found that the kurtosis of range and disparity coefficients is significantly larger than that of luminance coefficients. We used generalized Gaussian models to fit the empirical marginal distributions. We found that the marginal distribution of luminance coefficients have a shape parameter p between 0.6 and 0.8, while range and disparity coefficients have much smaller parameters p < 0.32, corresponding to a much higher peak. We also examined the conditional distributions of luminance, range and disparity coefficients. The magnitudes of luminance and range (disparity) coefficients show a clear positive correlation, which means, at a location with larger luminance variation, there is a higher probability of a larger range (disparity) variation. We also used generalized Gaussians to model the conditional distributions of luminance and range (disparity) coefficients. The values of the two shape parameters (p,s) reflect the observed luminance-range (disparity) dependency. As an example of the usefulness of luminance statistics conditioned on range statistics, we modified a well-known Bayesian stereo ranging algorithm using our natural scene statistics models, which improved its performance.