Matthew D. Gaubatz

A patch-based structural masking model with an application to compression

The ability of an image region to hide or mask a given target signal continues to play a key role in the design of numerous image processing and vision systems. However, current state-of-the-art models of visual masking have been optimized for artificial targets placed upon unnatural backgrounds. In this paper, we (1) measure the ability of natural-image patches in masking distortion; (2) analyze the performance of a widely accepted standard masking model in predicting these data; and (3) report optimal model parameters for different patch types (textures, structures, and edges). Our results reveal that the standard model of masking does not generalize across image type; rather, a proper model should be coupled with a classification scheme which can adapt the model parameters based on the type of content contained in local image patches. The utility of this adaptive approach is demonstrated via a spatially adaptive compression algorithm which employs patch-based classification. Despite the addition of extra side information and the high degree of spatial adaptivity, this approach yields an efficient wavelet compression strategy that can be combined with very accurate rate-control procedures.

Spatial quantization via local texture masking

Wavelet-based transform coding is well known for its utility in perceptual image compression. Psychovisual modeling has lead to a variety of perceptual quantization schemes, for efficient at-threshold compression. Successfully extending these models to supra-threshold compression, however, is a more difficult task. This work attempts to bridge the gap between at threshold modeling and supra-threshold compression by combining a spatially-selective quantization scheme, designed for at-threshold compression with simple MSE-based rate-distortion optimization. A psychovisual experiment is performed to determine how textured image regions can be used to mask quantization induced distortions. Texture masking results from this experiment are used to derive a spatial quantization scheme, which hides distortion in high-contrast image regions. Unlike many spatial quantizers, this technique requires explicit side information to convey contrast thresholds to generate step-sizes. A simple coder is presented that is designed that applies spatially-selective quantization to meet any rate constraints near and above threshold. This coder leverages this side information to reduce the rate required to code the quantized data. Compression examples are compared with JPEG-2000 examples with visual frequency weighting. When matched for rate, the spatially quantized images are highly competitive with and in some cases superior to the JPEG-2000 results in terms of visual quality.

Robust Rate-Control for Wavelet-Based Image Coding via Conditional Probability Models

Real-time rate-control for wavelet image coding requires characterization of the rate required to code quantized wavelet data. An ideal robust solution can be used with any wavelet coder and any quantization scheme. A large number of wavelet quantization schemes (perceptual and otherwise) are based on scalar dead-zone quantization of wavelet coefficients. A key to performing rate-control is, thus, fast, accurate characterization of the relationship between rate and quantization step size, the R-Q curve. A solution is presented using two invocations of the coder that estimates the slope of each R-Q curve via probability modeling. The method is robust to choices of probability models, quantization schemes and wavelet coders. Because of extreme robustness to probability modeling, a fast approximation to spatially adaptive probability modeling can be used in the solution, as well. With respect to achieving a target rate, the proposed approach and associated fast approximation yield average percentage errors around 0.5% and 1.0% on images in the test set. By comparison, 2-coding-pass rho-domain modeling yields errors around 2.0%, and post-compression rate-distortion optimization yields average errors of around 1.0% at rates below 0.5 bits-per-pixel (bpp) that decrease down to about 0.5% at 1.0 bpp; both methods exhibit more competitive performance on the larger images. The proposed method and fast approximation approach are also similar in speed to the other state-of-the-art methods. In addition to possessing speed and accuracy, the proposed method does not require any training and can maintain precise control over wavelet step sizes, which adds flexibility to a wavelet-based image-coding system

Distortion metrics for predicting authentication functionality of printed security deterrents

Document authentication is the process by which a unique identifier is assigned to each version of a printed document and is verified via an inspection procedure. One type of approach that has proven successful involves printing overt color-tile security deterrents, which can be scanned and analyzed to provide authenticity. For a variety of reasons discussed herein, it is advantageous to estimate whether or not a printed deterrent will authenticate without invoking the actual authentication process. Several algorithms are presented to predict the outcome of the authentication process. The area-over-the-curve (AOC) statistic is one of the tools used to characterize how well each candidate algorithm estimates authentication performance in the presence of distortions introduced via different print+scan paths. A surprising but useful result is that a no-reference metric yields the best performance: the AOC is approximately half that of all other tested methods, and is over five times smaller than the AOC achieved by state-of-the-art algorithms SSIM and VIF.

On the nearly scale-independent rank behavior of image quality metrics

Image quality metrics attempt to assess the perceived difference between an original and a distorted image. One aspect of quality assessment that is not yet well understood is the scale at which images are analyzed. This paper examines the effects that two scale-reducing operations, filtering and down-sampling, have on quality assessment. It has been shown that several popular quality metrics are equivalent to weighted measurements of mean-squared-error (MSE). Analysis is provided suggesting that the relative rankings of weighted MSE computations are not very sensitive to the scale of the input images; these predictions are verified experimentally using both weighted MSE-based and non-weighted-MSE-based assessment techniques. Featured algorithms include MSE, structural similarity index (SSIM), multi-scale SSIM (MSSIM), visual information fidelity (VIF) and visual signal-to-noise ratio (VSNR). Extensive testing on the LIVE database demonstrates that all algorithms except VSNR can be computed using images decimated by a factor of 6-8 without a substantial degradation in rank-order performance. This result illustrates that significant savings in processing are therefore possible.

Efficient Entropy Estimation Based on Doubly Stochastic Models for Quantized Wavelet Image Data

Under a rate constraint, wavelet-based image coding involves strategic discarding of information such that the remaining data can be described with a given amount of rate. In a practical coding system, this task requires knowledge of the relationship between quantization step size and compressed rate for each group of wavelet coefficients, the R-Q curve. A common approach to this problem is to fit each subband with a scalar probability distribution and compute entropy estimates based on the model. This approach is not effective at rates below 1.0 bits-per-pixel because the distributions of quantized data do not reflect the dependencies in coefficient magnitudes. These dependencies can be addressed with doubly stochastic models, which have been previously proposed to characterize more localized behavior, though there are tradeoffs between storage, computation time, and accuracy. Using a doubly stochastic generalized Gaussian model, it is demonstrated that the relationship between step size and rate is accurately described by a low degree polynomial in the logarithm of the step size. Based on this observation, an entropy estimation scheme is presented which offers an excellent tradeoff between speed and accuracy; after a simple data-gathering step, estimates are computed instantaneously by evaluating a single polynomial for each group of wavelet coefficients quantized with the same step size. These estimates are on average within 3% of a desired target rate for several of state-of-the-art coders

Printer-scanner identification via analysis of structured security deterrents

Device identification, the ability to discern the (separate) devices by which a document was produced and/or imaged, can be leveraged in the design of quality assurance (QA) systems as well as the practice of forensic analysis. It is shown that QA metrics associated with printed security markings provide a useful approach for performing multiple device identification, i.e., printer-scanner identification. While some previous methods have focused on properties of sensors to extract signatures from general image data, the proposed approach leverages the highly structured nature of color tile deterrents to predict device (combination) signatures based on a limited amount of information. Constraints introduced by the deterrent structure yield a relatively simple classification strategy with strong performance using a 10-dimensional feature vector. Sixteen printer-scanner combinations (composed from 4 printers and 4 scanners) are tested using this method. Results illustrate device signature prediction performance that is competitive with current state-of-the-art approaches based on physical models of the devices involved.

Spatially-selective quantization and coding for wavelet-based image compression

Recent developments in psychovisual modeling have led to improvements in wavelet-based coder performance. A spatially selective quantizer based on texture masking sensitivities is introduced, which hides distortion in high-contrast portions of images. Unlike other spatial quantization schemes, this method requires explicit side information to convey stepsizes. A simple coder is presented which leverages this side information to reduce the rate required to code the quantized data. Side information coding is also discussed. With respect to visual quality, this compression scheme performs competitively with a CSF-optimized JPEG-2000 coder at equivalent rates.

Ordering for Embedded Coding of Wavelet Image Data Based on Arbitrary Scalar Quantization Schemes

Many modern wavelet quantization schemes specify wavelet coefficient step sizes as continuous functions of an input step-size selection criterion; rate control is achieved by selecting an appropriate set of step sizes. In embedded wavelet coders, however, rate control is achieved simply by truncating the coded bit stream at the desired rate. The order in which wavelet data are coded implicitly controls quantization step sizes applied to create the reconstructed image. Since these step sizes are effectively discontinuous, piecewise-constant functions of rate, this paper examines the problem of designing a coding order for such a coder, guided by a quantization scheme where step sizes evolve continuously with rate. In particular, it formulates an optimization problem that minimizes the average relative difference between the piecewise-constant implicit step sizes associated with a layered coding strategy and the smooth step sizes given by a quantization scheme. The solution to this problem implies a coding order. Elegant, near-optimal solutions are presented to optimize step sizes over a variety of regions of rates, either continuous or discrete. This method can be used to create layers of coded data using any scalar quantization scheme combined with any wavelet bit-plane coder. It is illustrated using a variety of state-of-the-art coders and quantization schemes. In addition, the proposed method is verified with objective and subjective testing

Efficient, Low Complexity Encoding of Multiple, Blurred Noisy Downsampled Images Via Distributed Source Coding Principles

In a portable device, such as a digital camera, limitations on storage are an important consideration. In addition, due to constraints on the complexity of available hardware, image coding algorithms must be fairly simple in implementation. This work presents one such efficient method for coding multiple images of a scene, in a manner that complements a post-processing-based enhancement system. Super-resolution, image restoration and de-noising algorithms have demonstrated the ability to improve the quality of an image using multiple blurry, noisy copies of the same scene. This additional quality does not come without cost, however, since an image capture system must store each image. The proposed encoding scheme is derived from a general linear system model, and encodes multiple images of the same scene, with different amounts of blurring. It is also compared with a variety of methods based on current camera compression technology. For the tested images, this approach requires one-half the rate required by other methods at lower rates. In addition, for a small performance loss, it is essentially implementable without using any compression hardware