A general two-dimensional coding method is presented that allows recovery of data based on only a cropped portion of the code, and without knowledge of the carrier image. A description of both an encoding and recovery system is provided. Our solution involves repeating a payload with a fixed number of bits, assigning one bit to every symbol in the image - whether that symbol is data carrying or non-data carrying - with the goal of guaranteeing recovery of all the bits in the payload. Because the technique is applied to images, for aesthetic reasons we do not use fiducials, and do not employ any end-of-payload symbols. The beginning of the payload is determined by a phase code that is interleaved between groups of payload rows. The recovery system finds the phase row by evaluating candidate rows, and ranks confidence based on the sample variance. The target application is data-bearing clustered-dot halftones, so special consideration is given to the resulting checkerboard subsampling. This particular application is examined via exhaustive simulations to quantify the likelihood of unrecoverable bits and bit redundancy as a function of offset, crop window size, and phase code spacing.

This paper presents a novel method to recover planar projections (homographies) from perspectively distorted images of clustered-dot halftones. The method uses the variation of the local affine deformation of the halftone (estimated using a Fourier method) to recover the additional projective components. Evidence provided from simulated distortion and mobile capture shows that the approach provides an effective first step in the problem of using mobile device to recover stenographic information embedded in unknown halftone images, as pixel-accurate alignment is needed to recover the data.

In high-speed printed media inspection environments, image restoration pipelines play a critical role in establishing and continually evaluating performance. A key role of such systems is to understand, mitigate (and possibly remove) artifacts introduced by motion blur. An approach is proposed that uses barcodes to help calibrate a one-dimensional blur restoration pipeline. Techniques are demonstrated whereby the structure of barcode markings may be leveraged to estimate motion blur parameters, even under extreme blur conditions or when the barcode is unknown. In addition, a framework for comparing blur estimation procedures based on barcode readability is introduced. These techniques can be applied independently of one another, but together form a set of useful tools for blur restoration pipeline calibration. Within this framework, it is shown that a low-complexity blur estimation strategy demonstrates performance competitive with state-of-the-art approaches in term of speed and accuracy.

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.

In a print production system, the ability to match a printed document with its original electronic form enables services that improve robustness of the production process, such as content tracking mechanisms, or highly targeted quality assurance checks. One approach to this problem is to use overt markings that are later removed. This work, however, adopts a method that uses no markings, relying instead on fast image matching criteria. Though general image matching can be a difficult problem, constraints associated with the production environment allow construction of a simple, efficient solution to the matching problem. A reduced-reference quality assessment algorithm is presented that is used to match every document in a collection of printed and scanned documents with its original electronic form, based on down-sampling, equalization, quantization and per-pixel comparison. Using a 1085 image test set, a 100% matching rate is achievable by representing each image with only 5 bits-per-pixel at 8 dots-per-inch. The proposed method also compares favorably with other current reduced-reference quality assessment algorithms in terms of speed, storage and achieved matching rate.

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.

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.

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.

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.

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

Wavelet-based spatial quantization is a technique to compress image data that adapts the compression to the data in each region of an image. This approach is motivated because quantization with a single step-size does not result in a uniform visual effect across each spatial location; different types of image content mask quantization errors in different ways. While many spatial quantization techniques determine step-sizes via local activity measures, the proposed method induces local quantization distortion based on experiments that quantify human detection of this distortion as function of both the contrast and the type of the image data. Three types in particular, textures, structures and edges, are considered. A classifier is utilized to detect to which of these three categories a local region of image data belongs, and step-sizes are then derived based on the contrast and class of each region. Class and contrast data are conveyed to the coder with explicit side information. For images compressed at threshold, the proposed method requires 3-10 % less rate than a similar previous approach without classification, and on average produces images that are preferred by 2/3 of tested viewers.

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.

Real-time rate-control requires fast, accurate characterization of the relationship between wavelet subband step-size and rate. A rate-control algorithm is presented based on local properties of the function parameterizing this relationship, the rate-quantization or R-Q curve, for each subband. The behavior of this function is characterized by using the coder once, with step-sizes close to those that achieve the desired rate, which are determined entirely by a model-based technique. This solution requires a coder, a performance metric (which implies a subband-quantization strategy), and continuously differentiable (C/sup 1/) R-Q curves. This approach differs from some previous approaches in that it is based on local structure of R-Q curves instead of a global property, and does not require any training. The proposed scheme produces step-sizes that achieve rates typically less than half a percent away from the target rate. This method is fast, accurate, coder independent, requires no training, and can be combined with any subband-based quantization scheme.

Embedded image coding is a compression technique that yields bit streams robust to truncation. The first embedded wavelet-based compression algorithms were designed to optimize for mean-squared-error (MSE). More recent algorithms incorporate a variety of perceptually-based optimization criteria, which are implemented by selecting subband quantization step-sizes based on a target quality (and therefore rate). Since these step-sizes vary with rate, a bit stream optimized for a specific rate, if truncated, does not necessarily reflect the perceptual quality achievable at this lower rate. Constraints are given under which a wavelet-based perceptual model can generate an embedded image representation yielding perceptually derived results over a range of rates. This kind of embedding is implemented by strategically selecting the order in which subband bit planes are coded. When truncated, the resulting embedded streams provide perceptually tuned images.

"Red-eye" is a phenomenon that causes the eyes of flash photography subjects to appear unnaturally reddish in color. Though commercial solutions exist for red-eye correction, all of them require some measure of user intervention. A method is presented to automatically detect and correct redeye in digital images. First, faces are detected with a cascade of multi-scale classifiers. The red-eye pixels are then located with several refining masks computed over the facial region. The masks are created by thresholding per-pixel metrics, designed to detect red-eye artifacts. Once the redeye pixels have been found, the redness is attenuated with a tapered color desaturation. A detector implemented with this system corrected 95% of the red-eye artifacts in 200 tested images.