结合视觉感知的对象基嵌入式图像编码中的码率控制方法

结合人眼视觉感知特性,提出一种面向对象基嵌入式编码的码率控制算法.首先结合人眼视觉感知特性估计各对象的重要性并确定其编码优先级;然后依据优先级对视觉对象进行位平面建模和熵编码,得到各自独立的码流;最后基于率失真优化理论在给定码率下对对象码流进行优化截取和重装.仿真实验结果表明,所提算法可以对不同重要对象进行差异化编码和传输,与基于PCRD算法的码率控制方法相比,新算法能提高重构图像的整体视觉效果     

High-frequency error concealment in wavelet-based image coding

A new algorithm for high-frequency subband error concealment in wavelet-based picture coding is presented. It is based on a wavelet patch repetition approach: the LBG algorithm, given by Linde et al., is used to generate a codebook of patches and, according to a boundary distance measure, one of these patches is selected to mask the damaged area. Experiments show noteworthy results.     

An investigation of wavelet-based image coding using anentropy-constrained quantization framework

Wavelet image decompositions generate a tree-structured set of coefficients, providing an hierarchical data-structure for representing images. A new class of previously proposed image compression algorithms has focused on new ways for exploiting dependencies between this hierarchy of wavelet coefficients using “zero-tree” data structures. This paper presents a new framework for understanding the efficiency of one specific algorithm in this class we introduced previously and dubbed the space-frequency quantization (SFQ)-based coder. It describes, at a higher level, how the SFQ-based image coder of our earlier work can be construed as a simplified attempt to design a global entropy-constrained vector quantizer (ECVQ) with two noteworthy features: (i) it uses an image-sized codebook dimension (departing from conventional small-dimensional codebooks that are applied to small image blocks); and (ii) it uses an on-line image-adaptive application of constrained ECVQ (which typically uses off-line training data in its codebook design phase). The principal insight offered by the new framework is that improved performance is achieved by more accurately characterizing the joint probabilities of arbitrary sets of wavelet coefficients. We also present an empirical statistical study of the distribution of the wavelet coefficients of high-frequency bands, which are responsible for most of the performance gain of the new class of algorithms. This study verifies that the improved performance achieved by the new class of algorithms like the SFQ-based coder can be attributed to its being designed around one conveniently structured and efficient collection of such sets, namely, the zero-tree data structure. The results of this study further inspire the design of alternative, novel data structures based on nonlinear morphological operators     

A reduced-complexity image coding scheme using decision-directed wavelet-based contourlet transform

Recently the wavelet-based contourlet transform (WBCT) is adopted for image coding because it matches better image textures of different orientations. However, its computational complexity is very high. In this paper, we propose three tools to enhance the WBCT coding scheme, in particular, on reducing its computational complexity. First, we propose short-length 2-D filters for directional transform. Second, the directional transform is applied to only a few selected subbands and the selection is done by a mean-shift-based decision procedure. Third, we fine-tune the context tables used by the arithmetic coder in WBCT coding to improve coding efficiency and to reduce computation. Simulations show that, at comparable coded image quality, the proposed scheme saves over 92% computing time of the original WBCT scheme. Comparing to the conventional 2-D wavelet coding schemes, it produces clearly better subjective image quality.     

Majorization-minimization algorithms for wavelet-based image restoration.

Standard formulations of image/signal deconvolution under wavelet-based priors/regularizers lead to very high-dimensional optimization problems involving the following difficulties: the non-Gaussian (heavy-tailed) wavelet priors lead to objective functions which are nonquadratic, usually nondifferentiable, and sometimes even nonconvex; the presence of the convolution operator destroys the separability which underlies the simplicity of wavelet-based denoising. This paper presents a unified view of several recently proposed algorithms for handling this class of optimization problems, placing them in a common majorization-minimization (MM) framework. One of the classes of algorithms considered (when using quadratic bounds on nondifferentiable log-priors) shares the infamous "singularity issue" (SI) of "iteratively reweighted least squares" (IRLS) algorithms: the possibility of having to handle infinite weights, which may cause both numerical and convergence issues. In this paper, we prove several new results which strongly support the claim that the SI does not compromise the usefulness of this class of algorithms. Exploiting the unified MM perspective, we introduce a new algorithm, resulting from using l1 bounds for nonconvex regularizers; the experiments confirm the superior performance of this method, when compared to the one based on quadratic majorization. Finally, an experimental comparison of the several algorithms, reveals their relative merits for different standard types of scenarios.     

Robust hyperspectral image coding with channel-optimizedtrellis-coded quantization

This paper presents a wavelet-based hyperspectral image coder that is optimized for transmission over the binary symmetric channel (BSC). The proposed coder uses a robust channel-optimized trellis-coded quantization (COTCQ) stage that is designed to optimize the image coding based on the channel characteristics. This optimization is performed only at the level of the source encoder and does not include any channel coding for error protection. The robust nature of the coder increases the security level of the encoded bit stream, and provides a much higher quality decoded image. In the absence of channel noise, the proposed coder is shown to achieve a compression ratio greater than 70:1, with an average peak SNR of the coded hyperspectral sequence exceeding 40 dB. Additionally, the coder is shown to exhibit graceful degradation with increasing channel errors     

Locally adaptive wavelet-based image interpolation.

We describe a spatially adaptive algorithm for image interpolation. The algorithm uses a wavelet transform to extract information about sharp variations in the low-resolution image and then implicitly applies interpolation which adapts to the image local smoothness/singularity characteristics. The proposed algorithm yields images that are sharper compared to several other methods that we have considered in this paper. Better performance comes at the expense of higher complexity.     

Nonlinear wavelet transforms for image coding via lifting

We investigate central issues such as invertibility, stability, synchronization, and frequency characteristics for nonlinear wavelet transforms built using the lifting framework. The nonlinearity comes from adaptively choosing between a class of linear predictors within the lifting framework. We also describe how earlier families of nonlinear filter banks can be extended through the use of prediction functions operating on a causal neighborhood of pixels. Preliminary compression results for model and real-world images demonstrate the promise of our techniques.     

Fast wavelet-based image characterization for highly adaptive image retrieval

Adaptive wavelet-based image characterizations have been proposed in previous works for content-based image retrieval (CBIR) applications. In these applications, the same wavelet basis was used to characterize each query image: This wavelet basis was tuned to maximize the retrieval performance in a training data set. We take it one step further in this paper: A different wavelet basis is used to characterize each query image. A regression function, which is tuned to maximize the retrieval performance in the training data set, is used to estimate the best wavelet filter, i.e., in terms of expected retrieval performance, for each query image. A simple image characterization, which is based on the standardized moments of the wavelet coefficient distributions, is presented. An algorithm is proposed to compute this image characterization almost instantly for every possible separable or nonseparable wavelet filter. Therefore, using a different wavelet basis for each query image does not considerably increase computation times. On the other hand, significant retrieval performance increases were obtained in a medical image data set, a texture data set, a face recognition data set, and an object picture data set. This additional flexibility in wavelet adaptation paves the way to relevance feedback on image characterization itself and not simply on the way image characterizations are combined.     

Hidden Markov models for wavelet-based blind source separation.

In this paper, we consider the problem of blind source separation in the wavelet domain. We propose a Bayesian estimation framework for the problem where different models of the wavelet coefficients are considered: the independent Gaussian mixture model, the hidden Markov tree model, and the contextual hidden Markov field model. For each of the three models, we give expressions of the posterior laws and propose appropriate Markov chain Monte Carlo algorithms in order to perform unsupervised joint blind separation of the sources and estimation of the mixing matrix and hyper parameters of the problem. Indeed, in order to achieve an efficient joint separation and denoising procedures in the case of high noise level in the data, a slight modification of the exposed models is presented: the Bernoulli-Gaussian mixture model, which is equivalent to a hard thresholding rule in denoising problems. A number of simulations are presented in order to highlight the performances of the aforementioned approach: 1) in both high and low signal-to-noise ratios and 2) comparing the results with respect to the choice of the wavelet basis decomposition.     

High compression image coding via directional filtering

A new image coding technique is presented as derived from an image decomposition into a low frequency component and many high frequency directional components. The directional filters and their properties are introduced. Then the implementation of the directional decomposition and the selection of the information to be coded are described. The combination of transform domain coding of the low frequency component and spatial domain coding of the directional components led to acceptable results with compression ratios higher than 30 to 1.     

An EM algorithm for wavelet-based image restoration

This paper introduces an expectation-maximization (EM) algorithm for image restoration (deconvolution) based on a penalized likelihood formulated in the wavelet domain. Regularization is achieved by promoting a reconstruction with low-complexity, expressed in the wavelet coefficients, taking advantage of the well known sparsity of wavelet representations. Previous works have investigated wavelet-based restoration but, except for certain special cases, the resulting criteria are solved approximately or require demanding optimization methods. The EM algorithm herein proposed combines the efficient image representation offered by the discrete wavelet transform (DWT) with the diagonalization of the convolution operator obtained in the Fourier domain. Thus, it is a general-purpose approach to wavelet-based image restoration with computational complexity comparable to that of standard wavelet denoising schemes or of frequency domain deconvolution methods. The algorithm alternates between an E-step based on the fast Fourier transform (FFT) and a DWT-based M-step, resulting in an efficient iterative process requiring O(NlogN) operations per iteration. The convergence behavior of the algorithm is investigated, and it is shown that under mild conditions the algorithm converges to a globally optimal restoration. Moreover, our new approach performs competitively with, in some cases better than, the best existing methods in benchmark tests.     

Variational models for image colorization via chromaticity and brightness decomposition.

Colorization refers to an image processing task which recovers color in grayscale images when only small regions with color are given. We propose a couple of variational models using chromaticity color components to colorize black and white images. We first consider total variation minimizing (TV) colorization which is an extension from TV inpainting to color using chromaticity model. Second, we further modify our model to weighted harmonic maps for colorization. This model adds edge information from the brightness data, while it reconstructs smooth color values for each homogeneous region. We introduce penalized versions of the variational models, we analyze their convergence properties, and we present numerical results including extension to texture colorization.     

Lossless image coding via adaptive linear prediction andclassification

In past years, there have been several improvements in lossless image compression. All the recently proposed state-of-the-art lossless image compressors can be roughly divided into two categories: single and double-pass compressors. Linear prediction is rarely used in the first category, while TMW, a state-of-the-art double-pass image compressor, relies on linear prediction for its performance. We propose a single-pass adaptive algorithm that uses context classification and multiple linear predictors, locally optimized on a pixel-by-pixel basis. Locality is also exploited in the entropy coding of the prediction error. The results we obtained on a test set of several standard images are encouraging. On the average, our ALPC obtains a compression ratio comparable to CALIC while improving on some images     

Hierarchical image coding via cerebellar model arithmetic computers

A hierarchical coding system for progressive image transmission that uses the generalization and learning capability of CMAC (cerebellar model arithmetic computer or cerebellar model articulation controller) is described. Each encoder and decoder includes a set of CMACs having different widths of generalization region. A CMAC with a wider generalization region is used to learn a lower frequency component of the original image. The training signals for each CMAC are progressively transmitted to a decoder. Compression is achieved by decreasing the number of training signals for CMAC with a wider generalization region, and by making quantization intervals wider for CMAC with a smaller generalization region. CMACs in the decoder are trained on the training signals to be transmitted. The output is recursively added to the other so that the quality of image reconstruction is gradually improved. The proposed method, unlike the conventional hierarchical coding methods, uses no filtering technique in both decimation and interpolation processes, and has the following advantages: (i) it does not suffer from problems of blocking effect; (ii) the computation includes no multiplication; (iii) the coarsest reconstructed image is quickly produced; (iv) the total number of transmitted data is equal to the number of the original image pixels; (v) all the reconstructed images are equal to the original image in size; (vi) quantization errors introduced at one level can be taken into account at the next level, allowing lossless progressive image transmission.     

Fast multiresolution motion estimation algorithms for wavelet-based scalable video coding

Motion estimation and compensation in wavelet domain have received much attention recently. To overcome the inefficiency of motion estimation in critically sampled wavelet domain, the low-band-shift (LBS) method and the complete-to-overcomplete discrete wavelet transform (CODWT) method are proposed for motion estimation in shift-invariant wavelet domain. However, a major disadvantage of these methods is the computational complexity. Although the CODWT method has reduced the computational complexity by skipping the inverse wavelet transform and making the direct link between the critically sampled subbands and the shift-invariant subbands, the full search algorithm (FSA) increases it. In this paper, we proposed two fast multiresolution motion estimation algorithms in shift-invariant wavelet domain: one is the wavelet matching error characteristic based partial distortion search (WMEC-PDS) algorithm, which improves computational efficiency of conventional partial distortion search algorithms while keeping the same estimate accuracy as the FSA; another is the anisotropic double cross search (ADCS) algorithm using multiresolution-spatio-temporal context, which provides a significantly computational load reduction while only introducing negligible distortion compared with the FSA. Due to the multiresolution nature, both the proposed approaches can be applied to wavelet-based scalable video coding. Experimental results show the superiority of the proposed fast motion estimation algorithms against other fast algorithms in terms of speed-up and quality.     

Curved wavelet transform for image coding.

The conventional two-dimensional wavelet transform used in existing image coders is usually performed through one-dimensional (1-D) filtering in the vertical and horizontal directions, which cannot efficiently represent edges and lines in images. The curved wavelet transform presented in this paper is carried out by applying 1-D filters along curves, rather than being restricted to vertical and horizontal straight lines. The curves are determined based on image content and are usually parallel to edges and lines in the image to be coded. The pixels along these curves can be well represented by a small number of wavelet coefficients. The curved wavelet transform is used to construct a new image coder. The code-stream syntax of the new coder is the same as that of JPEG2000, except that a new marker segment is added to the tile headers. Results of image coding and subjective quality assessment show that the new image coder performs better than, or as well as, JPEG2000. It is particularly efficient for images that contain sharp edges and can provide a PSNR gain of up to 1.67 dB for natural images compared with JPEG2000.     

New method for reducing GOP-boundary artifacts in wavelet-based video coding

The conventional motion-compensated temporal wavelet transform using the 5/3 filter produces GOP-boundary artifacts, i.e., a drop in picture quality at the boundaries of groups of pictures (GOP). A simple and efficient method is proposed in this paper to reduce the GOP-boundary artifacts and improve the overall performance of the transform. With this method, a group of pictures to be temporally transformed is extended by boundary repetition to one side and by symmetrical extension to the other side. The sub-sampling rule changes from one level to another during the multi-level transform. In addition, a non-uniform quantization scheme is employed. This method does not require any additional computation or memory. Experimental results of video coding with six test sequences show that this method outperforms the conventional method, providing a coding gain of 0.27 dB in terms of average PSNR and up to 1.95 dB in terms of minimum PSNR.     

Robust face tracking via collaboration of generic and specific models.

Significant appearance changes of objects under different orientations could cause loss of tracking, "drifting." In this paper, we present a collaborative tracking framework to robustly track faces under large pose and expression changes and to learn their appearance models online. The collaborative tracking framework probabilistically combines measurements from an offline-trained generic face model with measurements from online-learned specific face appearance models in a dynamic Bayesian network. In this framework, generic face models provide the knowledge of the whole face class, while specific face models provide information on individual faces being tracked. Their combination, therefore, provides robust measurements for multiview face tracking. We introduce a mixture of probabilistic principal component analysis (MPPCA) model to represent the appearance of a specific face under multiple views, and we also present an online EM algorithm to incrementally update the MPPCA model using tracking results. Experimental results demonstrate that the collaborative tracking and online learning methods can handle large pose changes and are robust to distractions from the background.