In high efficiency video coding (HEVC) standard, residuals are partitioned into variable-size transform units, and integer cosine transforms (ICTs) of order-4, 8, 16, and 32 are adopted to optimize the coding performance. Order-64 ICT is not used due to its high complexity and limited gain in general coding applications. This paper proposes a set of low-complexity ICTs (LCICTs), from order-8 to order-64, which have fully factorizable structures. LCICTs achieve similar coding performance as the state-of-art methods on HEVC Test Model 13 while requiring lower complexity, e.g., 71% fewer multiplications than the partially factorizable ICTs in the order-64 case. Moreover, we find that order-64 ICTs achieve around 1% average bitrate reduction at high quantization parameters.

Pattern matching is widely used in signal processing, computer vision, and image and video processing. One efficient approach is to perform pattern matching in a transform domain that has good energy packing ability and so allows early rejection of most mismatched candidates. Calculating the transforms of pixels in sliding windows requires much computation, and so fast algorithms are employed. Existing methods require O(u) additions per pixel for projecting input pixels onto u 2D basis vectors. In this paper, we propose a new 2D transform, called asymmetric 2D Haar transform, and extend it to wavelet packets that contain exponentially large number of bases. A basis selection algorithm is then proposed to search for the optimal basis in the wavelet packets. A fast algorithm is also developed, which can compute u projection coefficients with only O(log u) additions per pixel. The results of experiments show that the proposed fast algorithm and the proposed transform can significantly accelerate the full-search-equivalent pattern matching process and outperform state-of-the-art methods.

An effective multi-focus image fusion method is proposed to generate an all-in-focus image with all objects in focus by merging multiple images. The proposed method first estimates focus maps using a novel combination of edge model and a traditional block-based focus measure. Then, a propagation process is conducted to obtain accurate weight maps based on a novel multi-matting model that makes full use of the spatial information. The fused all-in-focus image is finally generated based on a weighted-sum strategy. Experimental results demonstrate that the proposed method has state-of-the-art performance for multi-focus image fusion under various situations encountered in practice, even in cases with obvious misregistration.

In this work, a quality estimation based multi-focus image fusion method (QEBIF) is proposed. In this method, the all-in-focus image is generated by pixel-wise summarizing the multi-focus images with their estimated focus levels as weights. Since the visual quality of an image is highly correlated with its focus level, the visual quality is estimated to be the pre-measurements of focus levels. Via the guided filter, the pre-measurements are smoothed to form the final-measurement with edges in the multi-focus images preserved simultaneously. In addition, the confidence map is proposed to measure the reliability of different local regions. Experiments show that QEBIF method outperforms the other fusion methods, and its fusion results can well maintain the detailed information in the multi-focus images without suffering the ringing or blocking artifacts.

An effective unsupervised segmentation method is proposed to extract the object of interest from low depth of field images based on a novel L0regularized matting model. First of all, a multi-scale reblurring model is utilized, together with guided filter and morphological filter, to generate a trimap that roughly labels the focused and defocused regions. Then, an L0regularized matting model is proposed to obtain the accurate segmentation of the object of interest. Experimental results demonstrate that the proposed method achieves state-of-the-art performance for unsupervised segmentation under various situations, and is robust to noise.

A method to derive an order-2Ninteger cosine transform is presented in this paper. The proposed transform has orthogonal basis vectors and inherits the recursive structure from the discrete cosine transform such that an order-2Ntransform can be derived from an order-2(N-1transform. In this paper, transforms up to order 32 are derived and implemented into High Efficiency Video Coding reference model HM13.0. Compared with the core transform in HM13.0, the proposed transform has more flexibility and lower complexity for implementation while providing almost the same coding performance.

In this paper, we tackle the problem of no-reference image quality assessment (IQA). A learning-based IQA framework “VIDGIQA” is proposed, which extracts quality features from the input image and regresses the visual quality on these features. Since different distortions lead to different visual perceptions in the human visual system, distortion information is adopted to guide the feature learning process together with the human quality scores. Besides, a regression method is proposed to model and estimate the visual importance weights of all local regions, which can effectively improve the performance. More importantly, all these operations are integrated into one deep neural network, so that they can be jointly optimized and well cooperate with each other. Experiments were conducted to demonstrate the power of the proposed method on several datasets, including the LIVE dataset [1], the TID 2013 dataset [2], the LIVE multiply distorted IQA dataset [3], CSIQ [4] , and the LIVE in the wild image quality database [5]. The proposed method achieves 0.969 and 0.973 on the LIVE dataset [1] in terms of the spearman rank-order correlation coefficient and the Pearson linear correlation coefficient, respectively, which outperforms the state-of-the-art methods.

A new fusion scheme which generates high quality all-in-focus images is proposed. Most existing fusion schemes use block-based focus measures which do not have high precision. The proposed scheme employs a parametric edge model to develop a new focus measure which expresses the sharpness of a pixel with high precision. Then a focus map is generated to express the sharpness of every pixel in an image according to the content using a matting propagation method. Our proposed method is robust and doesn't suffer from blocking and ringing artifacts, which performs better than most multi-focus fusion algorithms.

In this paper, an efficient loop filter is proposed to restore the decoded frame for HEVC. The loop filtering problem is modeled as an inverse problem and solved by maximum a posteriori estimation. By modeling non-local block similarity into the MRF prior, the proposed filter can be efficiently derived by solving an optimization problem. Both temporal and spatial adaptivity of the filter are considered during the implementation. Experimental results show that up to 5.6% bit rate reduction can be achieved by the proposed loop filter when compared with HEVC reference software HM13.0.

In this paper, we propose a temporal layer adaptive loop filter (TLAF), which suppresses the quantization noise adaptively to the characteristics of frames at different temporal layer. The proposed filter unifies two different modes of Kuan's filter which is an efficient linear minimum mean square error (LMMSE) estimator. Block-based non-local Kuan's filter (BNLK) is applied on frames at lower temporal layer in transform domain while pixel-based non-local Kuan's filter (PNLK) is applied on frames at higher temporal layer in spatial domain. Experimental results show that when compared with HEVC main profile, the proposed loop filter achieves on average 3.1%, 2.1% and 2.1% bit rate reduction at mode all-intra, random-access and low-delay B respectively in combination with better visual quality around edges and textures.

The gray-code kernels (GCK) family, which has Walsh Hadamard transform on sliding windows as a member, is a family of kernels that can perform image analysis efficiently using a fast algorithm, such as the GCK algorithm. The GCK has been successfully used for pattern matching. In this paper, we propose that the G4-GCK algorithm is more efficient than the previous algorithm in computing GCK. The G4-GCK algorithm requires four additions per pixel for three basis vectors independent of transform size and dimension. Based on the G4-GCK algorithm, we then propose the segmented GCK. By segmenting input data into L(s) parts, the SegGCK requires only four additions per pixel for 3L(s) basis vectors. Experimental results show that the proposed algorithm can significantly accelerate the full-search equivalent pattern matching process and outperforms state-of-the-art methods.

Pattern matching is widely used in signal processing, computer vision, and image and video processing. Full search equivalent algorithms accelerate the pattern matching process and, in the meantime, yield exactly the same result as the full search. This paper proposes an analysis and comparison of state-of-the-art algorithms for full search equivalent pattern matching. Our intention is that the data sets and tests used in our evaluation will be a benchmark for testing future pattern matching algorithms, and that the analysis concerning state-of-the-art algorithms could inspire new fast algorithms. We also propose extensions of the evaluated algorithms and show that they outperform the original formulations.

Existing video coding standards use only 4 × 4 and 8 × 8 transforms for energy compaction. Recent research has found that the use of larger transforms, such as 16 × 16, together with the existing transforms can improve coding performance especially in high-definition (HD) videos which are becoming more and more common. This raises the interest of seeking high-performance higher-order transforms with low computation requirement. In this paper, a method to derive orthogonal integer cosine transforms is proposed. The order-2N transform is defined using the order-N transform. A family of these integer transforms, Loeffler, Ligtenberg, and Moschytz (LLM) integer cosine transform, is derived using this method. Its fast algorithm structure is the same as LLM fast discrete cosine transform (DCT) algorithm but requires integer operations only. This new family of transforms is not only very close to the DCT but also has excellent coding performance.

In this paper, we present a postprocessing method to tackle the single-image refocusing-and-defocusing problem. The proposed method can accomplish the tasks of focus-map estimation and image refocusing and defocusing. Given an image with a mixture of focused and defocused objects, we first detect the edges and then estimate the focus map based on the edge blurriness, which is depicted explicitly by a parametric model. The image refocusing problem is addressed in a blind deconvolution framework, where the image prior is modeled by using both global and local constraints. In particular, we correct the defocused blurry edges to sharp ones with the aid of the parametric edge model and then render this cue as a local prior to ensure the sharpness of the refocused image. Experimental results demonstrate that the proposed method performs well in producing visually plausible images with different focus effects from a single input.

In this paper, we present a simple yet effective method that takes advantage of the gradient information to accomplish the multiexposure image composition in both static and dynamic scenes. Given multiple images with different exposures, the proposed approach is capable of producing a pleasant tone-mapped-like high-dynamic-range image by compositing them seamlessly with the guidance of gradient-based quality assessment. In particular, two novel quality measures, namely, visibility and consistency, are developed based on the observations of gradient changes among different exposures. Experiments in various static and dynamic scenes are conducted to demonstrate the effectiveness of the proposed method.

As having multiple images of an object is practically convenient nowadays, to jointly align them is important for subsequent studies and a wide range of applications. In this paper, we propose a model-based approach to jointly align a batch of images of a face undergoing a variety of geometric and appearance variations. The principal idea is to model the non-rigid deformation of a face by means of a learned deformable model. Different from existing model-based methods such as Active Appearance Models, the proposed one does not rely on an accurate appearance model built from a training set. We propose a robust fitting method that simultaneously identifies the appearance space of the input face and brings the images into alignment. The experiments conducted on images in the wild in comparison with competing methods demonstrate the effectiveness of our method in joint alignment of complex objects like human faces.

Artifacts commonly exist in the coded image and video. Non-local Kuan's (NLK) filter has achieved the state-of-the-art performance on deartifacting the coded image, but there is no literature incorporating it into video scenarios. In the paper, we propose two loop filters, quadtree-based NLK (QNLK) filter and quadtree-based overcomplete NLK (QOCNLK) filter, for video coding. In the proposed techniques, NLK and overcomplete NLK filters are used to restore quantized residual transform coefficients, and quadtree-based signaling strategy is used for adaptive filtering control. Experimental results show that the proposed techniques achieve significant bitrate saving and visual quality improvement, compared with H.264/AVC High Profile.

In this paper, we propose a novel learning-based face hallucination framework built in the DCT domain, which can produce a high-resolution face image from a single low-resolution one. The problem is formulated as inferring the DCT coefficients in frequency domain instead of estimating pixel intensities in spatial domain. Our study shows that DC coefficients can be estimated fairly accurately by simple interpolation-based methods. AC coefficients, which contain the information of local features of face image, cannot be estimated well using interpolation. A simple but effective learning-based inference model is proposed to infer the ac coefficients. Experiments have been conducted to demonstrate the effectiveness of the proposed method in producing high quality hallucinated face images.

Pattern matching is a widely used procedure in signal processing, computer vision, image and video processing. Recently, methods using Walsh Hadamard Transform (WHT) and Gray-Code kernels (GCK) are successfully applied for fast transform domain pattern matching. This paper introduces strip sum on the image. The sum of pixels in a rectangle can be computed by one addition using the strip sum. Then we propose to use the orthogonal Haar transform (OHT) for pattern matching. Applied for pattern matching, the algorithm using strip sum requires O(log u) additions per pixel to project input data of size N × N onto u 2-D OHT basis while existing fast algorithms require O(u) additions per pixel to project the same data onto u 2-D WHT or GCK basis. Experimental results show the efficiency of pattern matching using OHT.

In this paper, we present a simple yet effective method that takes advantage of the gradient information to accomplish the multi-exposure image composition in both static and dynamic scenes. Given multiple images with different exposures, the proposed approach is capable of producing a pleasant tonemapped-like high dynamic range (HDR) image by compositing them seamlessly with the guidance of gradient-based quality assessment. Especially, two novel quality measures: visibility and consistency, are developed based on the observations of gradient changes among different exposures. Experiments in various static and dynamic scenes are conducted to demonstrate the effectiveness of the proposed method.

Transform is a very important coding tool in video coding. Simple order-16 Integer Transform, proposed in this paper, is a simple, low computational complexity transform but has high coding efficiency. It is simpler than other order-16 transforms previously proposed but it can achieve similar coding performance. It is shown that it significantly reduces the bit rate up to 18.49%. On average, it reduces the bit rate by 8.71%. It is comparable with other proposed transforms but significantly simpler.

Blurring and jaggy artifacts are the primal culprits that plague the current super-resolution techniques. In this paper, we propose a simple but effective approach which is capable of producing a pleasant artifact-free high-resolution image from a single low-resolution input. Specifically, we first magnify the low-resolution image to the desired resolution through structure adaptive interpolation to avoid jaggies. Then a salient edge directed deblurring scheme is introduced to remove the blurriness of the magnified image. Unlike previous work, we advocate solving the deblurring problem in an efficient manner with the aid of little user intervention. Our study shows that the blurring kernel can be estimated fairly well from the salient edges selected with user-drawn stroke based on a parametric edge model. Experiments are conducted to validate the effectiveness of the proposed method.

This paper proposes a fast algorithm for Walsh Hadamard Transform on sliding windows which can be used to implement pattern matching most efficiently. The computational requirement of the proposed algorithm is about 1.5 additions per projection vector per sample, which is the lowest among existing fast algorithms for Walsh Hadamard Transform on sliding windows.

Blocking artifacts exist in images and video sequences compressed to low bit rates using block discrete cosine transform (DCT) compression standards. In order to reduce blocking artifacts, a novel DCT domain technique is presented in this paper. Firstly, a new FIR Wiener filter which exploits the dependence of neighboring DCT coefficients based on the linear minimum mean-square-error (LMMSE) criterion is proposed. Then we apply the new FIR Wiener filter twice in a dual adaptive filtering structure to restore each quantized DCT coefficient. In addition, an efficient parameter estimation method is proposed for the designed filter. Experimental results show that the performance of the proposed method is comparable to the state-of-the-art methods but has low computational complexity.

Edge detection plays an important role in digital image processing. Based on the non-decimated wavelet which is shift-invariant, in this paper, we develop a new edge detecting technique using 3-D Hidden Markov Model. Our proposed model can not only capture the relationship of the wavelet coefficients inter-scale, but also consider the intra-scale dependence. A computationally efficient maximum likelihood (ML) estimation algorithm is employed to compute parameters and the hidden state of each coefficient is revealed by maximum a posteriori (MAP) estimation. Experimental results of natural images are provided to evaluate the algorithm. In addition, the proposed model has the potential to be an efficient multi-scale statistical modeling tool for other image or video processing tasks.

In this paper, the spatial properties of high-definition (HD) videos are investigated based on a large set of HD video sequences. Compared with lower resolution videos, the prediction errors of HD videos have higher correlation. Hence, we propose using 2-D order-16 transforms for HD video coding, which are expected to be more efficient to exploit this spatial property, and specifically propose two types of 2-D order-16 integer transforms, nonorthogonal integer cosine transform (ICT) and modified ICT. The former resembles the discrete cosine transform (DCT) and is approximately orthogonal, of which the transform error introduced by the nonorthogonality is proven to be negligible. The latter modifies the structure of the DCT matrix and is inherently orthogonal, no matter what the values of the matrix elements are. Both types allow selecting matrix elements more freely by releasing the orthogonality constraint and can provide comparable performance with that of the DCT. Each type is integrated into the audio and video coding standard (AVS) Enhanced Profile (EP) and the H.264 high profile (HP), respectively, and used adaptively as an alternative to the 2-D order-8 transform according to local activities. At the same time, many efforts have been devoted to further reducing the complexity of the 2-D order-16 transforms and specially for the modified ICT, a fast algorithm is developed and extended to a universal approach. Experimental results show that 2-D order-16 transforms provide significant performance improvement for both AVS enhanced profile and H.264 high profile, which means they can be efficient coding tools especially for HD video coding.

In this paper, we propose a novel learning-based face hallucination framework built in DCT domain, which can recover the high-resolution face image from a single low-resolution one. Unlike most previous learning-based work, our approach addresses the face hallucination problem from a different angle. In details, the problem is formulated as inferring DCT coefficients in frequency domain instead of estimating pixel intensities in spatial domain. Experimental results show that DC coefficients can be estimated fairly accurately by simple interpolation-based methods. AC coefficients, which contain the information of local features of face image, cannot be estimated well using interpolation. We propose a method to infer AC coefficients by introducing an efficient learning-based inference model. Moreover, the proposed framework can lead to significant savings in memory and computation cost since the redundancy of the training set is reduced a lot by clustering. Experimental results demonstrate that our approach is very effective to produce hallucinated face images with high quality.

In this paper, we address the problem of producing super-resolved image from a single low-resolution input. Unlike most previous work, the camera’s Point Spread Function (PSF) is not assumed to be known in advance and the single image super-resolution problem is formulated as a blind deconvolution problem under a MAP framework which can be optimized effectively in an iterative manner. Experimental results demonstrate that our method can successfully generate super-resolved image with high quality, both subjectively and objectively.

A fast video object segmentation algorithm is proposed in this paper. The algorithm utilizes the motion vectors from blocks with variable block sizes to identify background motion model and moving objects. Markov random field is used to model the foreground field to enhance spatial and temporal continuity of objects. To speed up the segmentation time, time-consuming spatial segmentation techniques are avoided. Instead, spatial information in the form of Walsh Hadamard transform coefficients is utilized to improve segmentation accuracy. Experimental results show that the proposed algorithm can effectively extract moving objects from different kind of video sequences. The computation time of the segmentation process is merely about 75 ms per CIF frame using a normal PC, allowing the algorithm to be applied in real-time applications such as video surveillance and conferencing.

In this paper, we propose a fast full-pel variable block size motion estimation algorithm called fast Walsh search in variable block size (FWS-VBS). FWS-VBS employs a new error measurement defined in Walsh-Hadamard domain, which is called partial sum-of-absolute difference, to identify likely mismatches. Mismatches are rejected by thresholding method and the thresholds are determined adaptively to cater for different activity levels in each block. Early termination techniques are employed to further reduce the number of candidates and modes to be searched of each block. Experimental results show that FWS-VBS performs equally well to the exhaustive full search algorithm and requires only about 10% of the computation time.

Integer cosine transform (ICT) is adopted by H.264/AVC for its bit-exact implementation and significant complexity reduction compared to the discrete cosine transform (DCT) with an impact in peak signal-to-noise ratio (PSNR) of less than 0.02 dB. In this paper, a new technique, named prescaled integer transform (PIT), is proposed. With PIT, while all the merits of ICT are kept, the implementation complexity of decoder is further reduced compared to corresponding conventional ICT, which is especially important and beneficial for implementation on low-end processors. Since not all PIT kernels are good in respect of coding efficiency, design rules that lead to good PIT kernels are considered in this paper. Different types of PIT and their target applications are examined. Both fixed block-size transform and adaptive block-size transform (ABT) schemes of PIT are also studied. Experimental results show that no penalty in performance is observed with PIT when the PIT kernels employed are derived from the design rules. Up to 0.2 dB of improvement in PSNR for all intra frame coding compared to H.264/AVC can be achieved and the subjective quality is also slightly improved when PIT scheme is carefully designed. Using the same concept, a variation of PIT, post-scaled integer transform, can also be potentially designed to simplify the encoder in some special applications. PIT has been adopted in audio video coding standard (AVS), Chinese National Coding standard.

Transform coding using the discrete cosine transform (DCT) has been widely used in image and video coding standards, but at low bit rates, the coded images suffer from severe visual distortions which prevent further bit reduction. Postprocessing can reduce these distortions and alleviate the conflict between bit rate reduction and quality preservation. Viewing postprocessing as an inverse problem, we solve it via a Bayesian approach. The distortion caused by coding is modeled as additive, spatially correlated Gaussian noise, while the original image is modeled as a high order Markov random field (MRF) based on the recently proposed fields of experts (FoE) framework. Experimental results show that the proposed method, in most cases, achieves higher PSNR gain than other methods and the processed images possess good visual quality.

Simplified order-16 integer cosine transform (ICT) has been proved to be an efficient coding tool especially for high-definition (HD) video coding and is much simpler than ICT and discrete cosine transform (DCT). To further reduce the computational complexity, a universal approach to developing fast algorithm mainly for, but not restricted to, simplified order-16 ICT is proposed in this paper. The fast algorithm developed by the proposed approach involves additions and shiftings only and can save about 90% of the computational time compared with matrix multiplication.

Transform coding using the discrete cosine transform (DCT) has been widely used in image and video coding standards, but at low bit rates, the coded images suffer from severe visual distortions which prevent further bit reduction. Postprocessing can reduce these distortions and alleviate the conflict between bit rate reduction and quality preservation. Viewing postprocessing as an inverse problem, we propose to solve it by the maximum a posteriori criterion. The distortion caused by coding is modeled as additive, spatially correlated Gaussian noise, while the original image is modeled as a high order Markov random field based on the fields of experts framework. Experimental results show that the proposed method, in most cases, achieves higher PSNR gain than other methods and the processed images possess good visual quality. In addition, we examine the noise model used and its parameter setting. The noise model assumes that the DCT coefficients and their quantization errors are independent. This assumption is no longer valid when the coefficients are truncated. We explain how this problem can be rectified using the current parameter setting.

In this paper we present a method for modeling a complex scene from a small set of images and then synthesizing a new view. Consider one of input images as the reference image, a small set of reliable pixel matches are initially selected and then propagated to neighboring pixels based on the clustering-based light invariant photoconsistency constraint and the data-driven depth smoothness constraint, which are integrated into a pixel matching quality function to deal with occlusions, light changes and depth discontinuity. The mismatched points in propagation are then iteratively rectified. Thus, the 3D structures of the scene are expressed by a large set of densely matched 3D points. For a new view, we initialize it by projecting these matched points. Both match propagation and mismatch rectification are used to complete the whole rendering task.

In this paper we propose a novel method for rendering a new view from multiple input images. A set of reliable pixel matches are initially selected and then propagated to neighboring pixels based on the piecewise smooth assumption in the spatial domain and the clustering-based photoconsistency constraint which is applied to handle pixel occlusion, partial pixel effects and lighting effects between images. Experimental results show that the proposed algorithm is promising and satisfactory.

The paper proposes a novel method for chin contour estimation. First, we estimate several possible chin locations. A modified Canny edge detector is then used to extract edges from the face image. Using these estimated chin edges and chin locations, we obtain a number of curves as candidates for the chin contour. A confidence score that measures the likeliness of a curve being the actual chin contour is used to select the most likely candidate. If the confidence score of the most likely candidate is high enough, then the estimated chin contour may be used as a feature for face recognition. Experimental results show that the proposed algorithm can extract the chin contours of human faces with good accuracy and the reliable estimated chin contours can improve the face recognition rate.

Images can be represented using as locations and parameters of edges based on an edge mode. The representation was shown to be effective to reduce coding errors of images coded using SPIHT in an image postprocessing method. However, the representation results several artifacts which include "smudge and halos" artifacts and stripping artifacts. We have found the causes of these artifacts and propose a revised representation that has these artifacts significantly suppressed Also, we shall demonstrate that the revised representation can further improve the performance of the image post-processing algorithm.

A novel method to estimate the chin contour which is the curve that separates the chin region from the neck region of a human face is proposed. The method assumes that the face and neck region has been separated from the background and the locations of eyes, mouth, and nose are known. The proposed algorithm first estimates several possible chin locations. A modified Canny edge detector is then used to extract edges that possess properties to qualify themselves as parts of the chin contour. Using these estimated chin edges and chin locations, we obtain a number of curves as candidates of the chin contour. A confidence score that measures the likeliness of a curve being the actual chin contour is used to select the most likely candidate. Experimental results show that the proposed algorithm can extract the chin contours of human faces of different facial expressions with good accuracy and the estimated chin contours with high confidence score is useful for face recognition.

An automatic algorithm to extract accurate eyebrow contours in a human face image is proposed. Based on the extracted eye corners, upper eye boundaries and the position points of the eyebrows, a feature region is limited to a local searching area. Separating the local searching area into several sub-areas and performing K-means clustering in each sub-area, we can obtain a rough region for each eyebrow. The rough contour obtained by tracking the rough eyebrow region is defined as an initial snake. Then the accurate eyebrow contour can be extracted by our improved snake method. Some prior knowledge about the shape of the object is used in both clustering and the snake method. Lastly, an application using the extracted eyebrow contours for face recognition is presented. The experimental results show that our method can detect the eyebrow contours with high accuracy and the eyebrow contour is useful for face recognition.

A straightforward way to illustrate a 3D model is to use a line drawing. Faces in a 2D line drawing provide important information for reconstructing its 3D geometry. Manifold objects belong to a class of common solids and most solid systems are based on manifold geometry. In this paper, a new method is proposed for finding faces from single 2D line drawings representing manifolds. The face identification is formulated based on a property of manifolds: each edge of a manifold is shared exactly by two faces. The two main steps in our method are (1) searching for cycles from a line drawing and (2) searching for faces from the cycles. In order to speed up the face identification procedure, a number of properties, most of which relate to planar manifold geometry in line drawings, are presented to identify most of the cycles that are or are not real faces in a drawing, thus reducing the number of unknown cycles in the second searching. Schemes to deal with manifolds with curved faces and manifolds each represented by two or more disjoint graphs are also proposed. The experimental results show that our method can handle manifolds previous methods can handle, as well as those they cannot.

In this paper, we propose a new postprocessing method for low bit-rate wavelet-based image coding which uses the technique of wavelet modulus maximum representation (WMMR). The edge degradation from wavelet-based coding is discussed under the overcomplete wavelet expansion, and interpreted as the distortion of wavelet modulus maxima, i.e., magnitude decays. Based on the empirical analysis and experimental results, we develop a set of compensation functions to restore the distorted wavelet modulus maxima of a coded image. Therefore, we can enhance the image quality for low bit-rate wavelet coding by reconstructing the coded image using the WMMR with improved visual quality and image fidelity (peak signal-to-noise ratio).

At low bit rates, wavelet-based image coding is superior to most traditional block-based methods in terms of visibility and severity of coding artifacts in coded images. However, the compressed images still suffer from obvious distortions around sharp edges, which are perceptually objectionable. In order to improve the image quality for low bit-rate wavelet-based image coding, we proposed a model-based edge-reconstruction algorithm for recovering the lossy edges in coded images. Our approach applies a general model to represent varieties of edges existing in an image. Based on this model, the edge degradation process due to quantization errors of wavelet coefficients is analyzed with the characterization of two kinds of artifacts at edges. We develop two operations, model-based edge approximation and Gaussian smoothing, to reconstruct distorted edges by reducing both artifacts respectively. The proposed method is able to improve the image quality in terms of both visual perception and image fidelity (peak signal-to-noise ratio) for most images coded by wavelet-based methods at low bit-rates.

This paper describes an asynchronous one-dimensional order-8 integer cosine transform chip, which can calculate either the forward or inverse transforms. The chip's performance is maximized with a fast computation algorithm and the self-timed circuit technique. The basic self-timed block is a microcoded programmable processor. Eight of these processors are used and achieve a data rate of up to 50 MHz in 0.7-/spl mu/m CMOS technology. If the delay in the handshake circuit can be minimized, the design suggests that asynchronous techniques are a feasible alternative to synchronous designs.

Summary form only given. At low bit rates, wavelet-based image coding is superior to most traditional block-based coding methods in that it does not introduce perceptually annoying blocking artifacts. However, the reconstructed images still suffer various coding artifacts at edges and continuous regions which are perceptually objectionable. Post-processing is an effective technique for improving image quality and, at the same time, keeping the compatibility with the existing encoder and decoder. In this work, we propose a new post-processing method for low bit-rate wavelet-based image coding using the techniques of overcomplete wavelet representation and multiscale edge characterization (Mallat 1992).

Low bit-rate image coding brings about an obvious degradation to the compressed images, among which, the distortions at the edges are particular objectionable. A model-based edge reconstruction algorithm is proposed for wavelet-based image coding at low bit-rate. Our approach applies a general model to represent the variety of edges existing in an image. Based on this model, the problem of edge reconstruction is formulated as finding the original edge model parameters from the lossy image. The proposed method is able to improve the subjective visual quality and fidelity (PSNR) of images coded by wavelet-based coding using zerotree quantization.

The DC coefficient restoration scheme is a technique which can be used to increase the compression ability of transform image coding by not transmitting the DC coefficients but estimating them from the transmitted AC component. In the last decade, the minimum edge difference criterion is used in the scheme. However the criterion fails at the locations where the discontinuities are along the block boundaries and therefore results in observable blocking effect around these locations or higher bit rate. We propose a new criterion using the maximum a posterior (MAP) estimation technique which preserves the discontinuities during the DC coefficients restoration and solves the blocking effects in the restored images.

At low bit-rates, wavelet-based image coding demonstrates some advantages over most traditional block-based coding methods in that it does not introduce perceptually annoying blocking artifacts in coded images. However, the coded images still suffer various coding artifacts at edges and continuous regions, which are disturbing to human perception. A new post-processing method is proposed to improve the image quality for low bit-rate wavelet-based image coding using the technique of signal characterization from multiscale edges. First, we analyzed the edge degradation process based on an edge model. Then the edge degradation is characterized in terms of the distortions of modulus maxima of the dyadic wavelet transform. Through some experiments on a set of images, a group of compensation functions are developed for modulus maxima restoration. Thus, from the restored modulus maxima and with some appropriate constraints, we can reconstruct an image with improved visual quality and image fidelity (PSNR).

The DC coefficient restoration scheme is a technique which can be used to increase the compression ability of transform image coding by not transmitting the DC coefficients but estimating them from the transmitted AC component. We have previously proposed a nonlinear estimation criterion using the maximum a posterior (MAP) estimation technique which preserves the discontinuities during the DC coefficients restoration and solves the blocking effect in the restored images. However we also found that the performance of the nonlinear criterion is affected by the quantization error in the transmitted AC coefficients. In this paper, we combine the nonlinear criterion with the AC coefficient restoration technique. The new method preserves the discontinuities of the images and also reduces the blocking effect in the restored images in the present of quantization error in the AC coefficients.

This paper describes a LSI implementation of 1-D order-8 integer cosine transform (ICT) which can calculate either forward or reverse transformation. It is a standard-cell based design using 0.7 /spl mu/m CMOS SLP DLM process. The chip's performance is maximized with the fast computation algorithm and self-timed circuit technique. It consists of eight parallel self-timed pipelines. Each self-timed block is designed based on 2-phase handshaking protocol and variable delay concept. The die size is 5.7/spl times/4.1 mm with about 76 K transistors. This chip supports 16-bit I/O data and its data rate is up to 60 MHz.

The usual attractor coding technique is to partition a given image into a number of non-overlapping range blocks. Each block of the partition is expressed as the contractive transformation of another part of the image. However, the non-overlapping partitioning induces blocking artifacts which is highly disturbing to the human visual system. In this work, a novel coding scheme using iterated function systems with lapped range blocks (LPIFS) is proposed. Each range block laps with its adjacent blocks through a weighing window which has diminishing magnitudes towards its borders. In order to avoid blurring of image details, the transformation parameters are computed such that the aliasing error is compensated. The contractivity of the proposed transformation is also proved. Experiments show a very significant improvement of the visual quality of the decoded images with nearly no loss in image details.

A stroke order and number free method for on-line recognition of Chinese characters is proposed. Both input characters and the model characters are represented with complete attributed relational graphs (ARGs). The ARGs of the model base are built according to the human knowledge of the segment relation structure of Chinese characters. For the recognition purpose, an optimal matching measure between two ARGs is defined, and the graph matching is formulated as a search problem of finding the minimum cost path in a state space tree, using the A * algorithm. Moreover, to reduce the search time of the A *, besides a heuristic estimate, a novel strategy is employed which again uses the human knowledge of the segment position structure of Chinese characters to prune the tree. Our experimental results demonstrate the efficiency of the proposed method.

A structural method for on-line recognition of Chinese characters is proposed, which is stroke order and stroke number free. Both input characters and the model characters are represented with complete attributed relational graphs (ARGs). A new optimal matching measure between two ARGs is defined. Classification of an input character can be implemented by matching its ARG against every ARG of the model base. The matching procedure is formulated as a search problem of finding the minimum cost path in a state space tree, using the A* algorithm. In order to speed up the search of the A*, besides a heuristic estimate, a novel strategy that utilizes the geometric position information of stroke segments of Chinese characters to prune the tree is employed. The efficiency of our method is demonstrated by the promising experimental results.

Methods of employing a combination of two recently reported simple transforms (High Correlation Transform and Low Correlation Transform) using block adaptive transform coding with spatial domain activity classification are examined. It is found that for small block size, (viz. 8×8), increased coding efficiency can be had by employing a combination of the two new transforms, particularly for high activity images. The performance of such systems for small block size is found to be close, and sometimes superior, to that of a more conventional block classification adaptive transform coder employing the more complex real number cosine transform.

Telephone channels restrict the bandwidth of speech signals to within the range of 0.3 to 3.4 kHz and in doing so cause significant attenuation and distortion in unvoiced sounds with resulting loss of intelligibility and quality. A bandwidth compression system is proposed in this paper, whereby 0.3 to 7.6 kHz band limited speech is frequency mapped into the 0.3 to 3.4 kHz telephonic bandwidth range and is subsequently digitized prior to transmission. The received signal is decoded and spectrally expanded using a corresponding frequency de-mapping process. Consideration is given to the digital encoding of the compressed signals for bit rates between 16 and 32 kbits/s. We find that the quality of the recovered speech is improved compared to band limited telephonic speech.