No-Reference Quality Assessment for Screen Content Images Using Visual Edge Model and AdaBoosting Neural Network

In this paper, a competitive no-reference metric is proposed to assess the perceptive quality of screen content images (SCIs), which uses the human visual edge model and AdaBoosting neural network. Inspired by the existing theory that the edge information which reflects the visual quality of SCI is effectively captured by the human visual difference of the Gaussian (DOG) model, we compute two types of multi-scale edge maps via the DOG operator firstly. Specifically, two types of edge maps contain contour and edge information respectively. Then after locally normalizing edge maps, L -moments distribution estimation is utilized to fit their DOG coefficients, and the fitted L -moments parameters can be regarded as edge features. Finally, to obtain the final perceptive quality score, we use an AdaBoosting back-propagation neural network (ABPNN) to map the quality-aware features to the perceptual quality score of SCIs. The reason why the ABPNN is regarded as the appropriate approach for the visual quality assessment of SCIs is that we abandon the regression network with a shallow structure, try a regression network with a deep architecture, and achieve a good generalization ability. The proposed method delivers highly competitive performance and shows high consistency with the human visual system (HVS) on the public SCI-oriented databases.

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.

Efficient stereoscopic ranging via stochastic sampling of match quality

We present an efficient method that computes dense stereo correspondences by stochastically sampling match quality values. Nonexhaustive sampling facilitates the use of quality metrics that take unique values at noninteger disparities. Depth estimates are iteratively refined with a stochastic cooperative search by perturbing the estimates, sampling match quality, and reweighting and aggregating the perturbations. The approach gains significant efficiencies when applied to video, where initial estimates are seeded using information from the previous pair in a novel application of the Z-buffering algorithm. This significantly reduces the number of search iterations required. We present a quantitative accuracy evaluation wherein the proposed method outperforms a microcanonical annealing approach by Barnard and a cooperative approach by Zitnick and Kanade , while using fewer match quality evaluations than either. The approach is shown to have more attractive memory usage and scaling than alternatives based on exhaustive sampling.

Adaptive bilateral filter for sharpness enhancement and noise removal

In this paper, we present the adaptive bilateral filter (ABF) for sharpness enhancement and noise removal. The ABF sharpens an image by increasing the slope of the edges without producing overshoot or undershoot. It is an approach to sharpness enhancement that is fundamentally different from the unsharp mask (USM). This new approach to slope restoration also differs significantly from previous slope restoration algorithms in that the ABF does not involve detection of edges or their orientation, or extraction of edge profiles. In the ABF, the edge slope is enhanced by transforming the histogram via a range filter with adaptive offset and width. The ABF is able to smooth the noise, while enhancing edges and textures in the image. The parameters of the ABF are optimized with a training procedure. ABF restored images are significantly sharper than those restored by the bilateral filter. Compared with an USM based sharpening method-the optimal unsharp mask (OUM), ABF restored edges are as sharp as those rendered by the OUM, but without the halo artifacts that appear in the OUM restored image. In terms of noise removal, ABF also outperforms the bilateral filter and the OUM. We demonstrate that ABF works well for both natural images and text images.

Development of the local maximum variety of ranked-order filters

A form of ranked-order filters is introduced as the local maximum filter. The construction of the local maximum filter is described, followed by a discussion of its function and some of its more important properties, and an example application of a two-dimensional local maximum filter is provided to illustrate the detection of single-pixel targets against a cloud clutter background. The closing discussion provides a mathematical development of the filter.

Developments in generalized ranked-order filters

A general formulation of ranked-order filters is developed in two parts: part 1, signal-to-noise-ratio analysis and part 2, construction and analysis of a ranked-order-filter function based on a mathematical logic approach. The filter function is analyzed to define the structure of filter roots for one-dimensional (1-D) and two-dimensional (2-D) window filters as data patterns that are invariant of the filter. The 1-D and 2-D coded window filters defined for roots of repeated patterns of binary data are defined and analyzed. The analysis concludes with an application of the coded window filter to a computer-generated 2-D noisy image containing a binary pattern and an application for feature extraction by a 2-D filter constrained by a predicate function to select only fixed-point root data structures.

Nonlinear image estimation using piecewise and local image models

We introduce a new approach to image estimation based on a flexible constraint framework that encapsulates meaningful structural image assumptions. Piecewise image models (PIMs) and local image models (LIMs) are defined and utilized to estimate noise-corrupted images, PIMs and LIMs are defined by image sets obeying certain piecewise or local image properties, such as piecewise linearity, or local monotonicity. By optimizing local image characteristics imposed by the models, image estimates are produced with respect to the characteristic sets defined by the models. Thus, we propose a new general formulation for nonlinear set-theoretic image estimation. Detailed image estimation algorithms and examples are given using two PIMs: piecewise constant (PICO) and piecewise linear (PILI) models, and two LIMs: locally monotonic (LOMO) and locally convex/concave (LOCO) models. These models define properties that hold over local image neighborhoods, and the corresponding image estimates may be inexpensively computed by iterative optimization algorithms. Forcing the model constraints to hold at every image coordinate of the solution defines a nonlinear regression problem that is generally nonconvex and combinatorial. However, approximate solutions may be computed in reasonable time using the novel generalized deterministic annealing (GDA) optimization technique, which is particularly well suited for locally constrained problems of this type. Results are given for corrupted imagery with signal-to-noise ratio (SNR) as low as 2 dB, demonstrating high quality image estimation as measured by local feature integrity, and improvement in SNR.

Application of non-linear filters based on the median filter to experimental and simulated multiunit neural recordings

Two non-linear, high-pass filters based on the median filter are proposed and tested as substitutes for linear filtering in applications involving multiunit neural recordings. The first, the median-based high-pass (MH) filter, operates by subtracting the output from the input of the median filter; it is aimed at preserving the shape of the impulses. The second, the negative median-based high-pass (NMH) filter, sets at zero the positive values in the output of the MH filter; it is aimed at transforming the impulses into monophasic waves placed on a flat baseline. When applied to experimental recordings and to a template action potential, the two median-based filters clearly outperformed two corresponding procedures based on a linear filter (moving-average filter). They did not produce appreciable distortions of the impulses, whereas their two counterparts induced or enlarged lateral lobes, as is the rule for linear high-pass filters. The recording display was much improved and impulse identification was made easier. When the two filters were applied to simulated recordings and the mean output was estimated by averaging and cross-correlation, a certain degree of performance deterioration was assessed in conditions of sustained activity and/or noise, with a resulting growing similarity to the mean output of the two corresponding, moving-average-based filters.

Order statistic-neural network hybrid filters for gamma camera-bremsstrahlung image restoration

An order statistic and neural network hybrid filter (OSNNH) is proposed for the restoration of gamma camera images using the measured modulation transfer function. Planar images of beta-emitting radionuclides are used to evaluate the filter because they exhibit higher degradation than images of single photon emitters due to increased photon scattering and collimator septal penetration. The filter performance is quantitatively evaluated and compared to that of the Wiener filter by investigating the relationship between the externally measured counts from sources of phosphorous-32 ((32)P) at various depths in water. An effective linear attenuation coefficient for (32)P is determined to be equal to 0.13 cm(-1) and 0.14 cm(-1) for the OSNNH and the Wiener filters, respectively. Evaluation of phantom and patient filtered images demonstrates that the OSNNH filter avoids ring effects caused by the ill-conditioned blur matrix and noise overriding caused by matrix inversion, typical of other restoration filters such as the Wiener.