A video compression scheme with optimal bit allocation amongsegmentation, motion, and residual error

We present a theory for the optimal bit allocation among quadtree (QT) segmentation, displacement vector field (DVF), and displaced frame difference (DFD). The theory is applicable to variable block size motion-compensated video coders (VBSMCVC), where the variable block sizes are encoded using the QT structure, the DVF is encoded by first-order differential pulse code modulation (DPCM), the DFD is encoded by a block-based scheme, and an additive distortion measure is employed. We derive an optimal scanning path for a QT that is based on a Hilbert curve. We consider the case of a lossless VBSMCVC first, for which we develop the optimal bit allocation algorithm using dynamic programming (DP). We then consider a lossy VBSMCVC, for which we use Lagrangian relaxation, and show how an iterative scheme, which employs the DP-based solution, can be used to find the optimal solution. We finally present a VBSMCVC, which is based on the proposed theory, which employs a DCT-based DFD encoding scheme. We compare the proposed coder with H.263. The results show that it outperforms H.263 significantly in the rate distortion sense, as well as in the subjective sense.     

An edge detection algorithm using multi-state adalines

An edge detection algorithm using multi-state adaptive linear neurons (ADALINES) is presented. Although the tri-state ADALINE is only considered in this work, general multi-state input vectors with extreme values are shown to be linearly separable from the rest of the vectors with the same dimension. The input state of each ADALINE is defined using the local mean in a predefined mask. In addition to the binary input states ± 1, the 0 input state is introduced for controlling the noise effect. If the input pattern matches one of the predefined edge patterns, the corresponding pixel is detected as an edge pixel. Experimental results are shown where the proposed detector is compared with both the Canny and LOG edge detectors.     

Iterative maximum likelihood displacement field estimation inquantum-limited image sequences

We develop an algorithm for obtaining the maximum likelihood (ML) estimate of the displacement vector field (DVP) from two consecutive image frames of an image sequence acquired under quantum-limited conditions. The estimation of the DVF has applications in temporal filtering, object tracking, stereo matching, and frame registration in low-light level image sequences as well as low-dose clinical X-ray image sequences. In the latter case, a controlled X-ray dosage reduction may be utilized to lower the radiation exposure to the patient and the medical staff. The quantum-limited effect is modeled as an undesirable, Poisson-distributed, signal-dependent noise artifact. A Fisher-Bayesian formulation is used to estimate the DVF and a block component search algorithm is employed in obtaining the solution. Several experiments involving a phantom sequence and a teleconferencing image sequence with realistic motion demonstrate the effectiveness of this estimator in obtaining the DVF under severe quantum noise conditions (20-25 events/pixel).     

Space-variant blur deconvolution and denoising in the dual exposure problem

In this paper we propose a space-variant blur estimation and effective denoising/deconvolution method for combining a long exposure blurry image with a short exposure noisy one. The blur in the long exposure shot is mainly caused by camera shake or object motion, and the noise in the underexposed image is introduced by the gain factor applied to the sensor when the ISO is set to an high value. Due to the space variant degradation, the image pair is divided into overlapping patches for processing. The main idea in the deconvolution algorithm is to incorporate a combination of prior image models into a spatially-varying deblurring/denoising framework which is applied to each patch. The method employs a kernel and parameter estimation method to choose between denoising or deblurring each patch. Experiments on both synthetic and real images are provided to validate the proposed approach.     

Content-Aware Resource Allocation and Packet Scheduling for Video Transmission over Wireless Networks

A cross-layer packet scheduling scheme that streams pre-encoded video over wireless downlink packet access networks to multiple users is presented. The scheme can be used with the emerging wireless standards such as HSDPA and IEEE 802.16. A gradient based scheduling scheme is used in which user data rates are dynamically adjusted based on channel quality as well as the gradients of a utility function. The user utilities are designed as a function of the distortion of the received video. This enables distortion-aware packet scheduling both within and across multiple users. The utility takes into account decoder error concealment, an important component in deciding the received quality of the video. We consider both simple and complex error concealment techniques. Simulation results show that the gradient based scheduling framework combined with the content-aware utility functions provides a viable method for downlink packet scheduling as it can significantly outperform current content-independent techniques. Further tests determine the sensitivity of the system to the initial video encoding schemes, as well as to non-real-time packet ordering techniques.     

Stochastic methods for joint registration, restoration, and interpolation of multiple undersampled images.

Using a stochastic framework, we propose two algorithms for the problem of obtaining a single high-resolution image from multiple noisy, blurred, and undersampled images. The first is based on a Bayesian formulation that is implemented via the expectation maximization algorithm. The second is based on a maximum a posteriori formulation. In both of our formulations, the registration, noise, and image statistics are treated as unknown parameters. These unknown parameters and the high-resolution image are estimated jointly based on the available observations. We present an efficient implementation of these algorithms in the frequency domain that allows their application to large images. Simulations are presented that test and compare the proposed algorithms.     

Optimized compressive sampling for passive millimeter-wave imaging

In this paper, we briefly describe a single detector passive millimeter-wave imaging system, which has been previously presented. The system uses a cyclic sensing matrix to acquire incoherent measurements of the observed scene and then reconstructs the image using a Bayesian approach. The cyclic nature of the sensing matrix allows for the design of a single unified and compact mask that provides all the required random masks in a convenient way, such that no mechanical mask exchange is needed. Based on this setup, we primarily propose the optimal adaptive selection of sampling submasks out of the full cyclic mask to obtain improved reconstruction results. The reconstructed images show the feasibility of the imaging system as well as its improved performance through the proposed sampling scheme.     

Bayesian Compressive Sensing Using Laplace Priors

In this paper, we model the components of the compressive sensing (CS) problem, i.e., the signal acquisition process, the unknown signal coefficients and the model parameters for the signal and noise using the Bayesian framework. We utilize a hierarchical form of the Laplace prior to model the sparsity of the unknown signal. We describe the relationship among a number of sparsity priors proposed in the literature, and show the advantages of the proposed model including its high degree of sparsity. Moreover, we show that some of the existing models are special cases of the proposed model. Using our model, we develop a constructive (greedy) algorithm designed for fast reconstruction useful in practical settings. Unlike most existing CS reconstruction methods, the proposed algorithm is fully automated, i.e., the unknown signal coefficients and all necessary parameters are estimated solely from the observation, and, therefore, no user-intervention is needed. Additionally, the proposed algorithm provides estimates of the uncertainty of the reconstructions. We provide experimental results with synthetic 1-D signals and images, and compare with the state-of-the-art CS reconstruction algorithms demonstrating the superior performance of the proposed approach.     

Detection and removal of anomalies in digitized animation film

In this article, the problem of detecting and removing anomalies in digitized animation film is addressed. The impetus of this article comes from the motion picture industry, where several studios are rereleasing vintage film to the public which are often accompanied by visual degradation in the film color, grain, and overall quality. These degradations, or anomalies, can be seen as unwanted visual artifacts that are usually larger than one pixel in size and appear in only one frame. One of the novelties of this article is that the restoration of animation film, as opposed to “real-world” image sequences, is investigated. It often contains additional artifacts and raises other issues not found in most sequences. These artifacts are first defined, and the appropriate steps for their detection and removal are described. Experiments with the proposed algorithm were performed using scenes from the animation film Fantasia, courtesy of Walt Disney Feature Animations, and are discussed in detail. Although our algorithm was developed for the removal of artifacts on animation film, aspects of it can be applied to nonanimated film. © 1998 John Wiley & Sons, Inc. Int J Imaging Syst Technol, 9, 283–293, 1998