A cluster of stunning style transfer approaches evolve to include single style transfer and multi-style transfer these years. However, few studies consider the style consistency between identical semantic objects in style images and the content image respectively. Especially for multi-style transfer, the merged style is obtained through a simple linear combination with given weights. So, the textures of each source style are mixed, which makes the result lack aesthetic value. To overcome this problem, we propose a 3S-Net to achieve semantic-aware style transfer mainly by Two-Step Semantic Instance Normalization (2SSIN) and Semantic Style Swap. 3S-Net can automatically accomplish the procedure of matching semantic objects between style images and content image. Besides, our method is adaptive and flexible. It succeeds in single style transfer and multi-style transfer from a semantic perspective, even ROI (Region of Interest) style transfer with user’s preference.

Stylizing images as paintings has been a popular computer vision technique for a long time. However, most studies only consider the art styles known today, and rarely have investigated styles that have not been painted yet. We fill this gap by projecting the high-dimensional style space of Convolutional Neural Network features to the latent low-dimensional style manifold space. It is worth noting that in our visualized space, simple style linear interpolation is enabled to generate new artistic styles that would revolutionize the future of art in technology. We propose a model of an Interactive Style Space (ISS) to prove that in a manifold style space, the unknown styles are obtainable through interpolation of known styles. We verify the correctness and feasibility of our Interactive Style Space (ISS) and validate style interpolation within the space.

In advanced Peer-to-Peer delivery systems, each user downloads a video stream and at the same time uploads the same stream to other users. In live streaming, scalable video streaming is also proposed to allow partial decoding of the video streaming at a reduced resolution, frame-rate or quality, adapting to different display requirements and receptions conditions of heterogeneous receivers. In live streaming applications, Network Coding (NC) has the potential to bring substantially higher throughput while reducing transmission delays. However, the bandwidth efficiency of NC transmission is still a problem to live media streaming due to the lack of synchronization among peers. To solve this problem, we introduce a scalable streaming system, which includes a layer selection algorithm and a distributed packet scheduling algorithm. The proposed system is tested by streaming a scalable video over a peer-to-peer network. The experimental results confirm that the proposed algorithm achieves better video quality, better delivery ratio, and lower transmission redundancy.

Over the last decade, the emergence of new multimedia devices has motivated the research on efficient media streaming mechanisms that can adapt to dynamic network conditions and heterogeneous devices' capabilities. Network coding (NC) as a rateless code has been applied to collaborative media streaming applications and brings substantial improvements regarding throughput and delay in collaborative media streaming applications. However, little attention has been given to the recoverability of encoded data, especially for the streaming with a strict deadline. When a receiver obtains insufficient packets, it is impossible for receivers to recover any original packets. This in turn leads to severe quality of experience. In this paper, we solve the unrecoverable transmission by determining a scalable layer subscription and scheduling strategy. This scalable layer subscription algorithm is treated as a video quality maximization problem in multiple generations. Then this problem is solved using a dynamic programming algorithm. Experimental results confirm that the proposed algorithm brings better data recoverability and a better quality of service in terms of better video quality, delivery ratio, lower redundancy rate, and significantly lower superfluous video packet rate under different network sizes than conventional random-push schemes.

A large number of color filter arrays (CFAs), periodic or aperiodic, have been proposed. To reconstruct images from all different CFAs and compare their imaging quality, a universal demosaicking method is needed. This paper proposes a new universal demosaicking method based on inter-pixel chrominance capture and optimal demosaicking transformation. It skips the commonly used step to estimate the luminance component at each pixel, and thus, avoids the associated estimation error. Instead, we directly use the acquired CFA color intensity at each pixel as an input component. Two independent chrominance components are estimated at each pixel based on the inter-pixel chrominance in the window, which is captured with the difference of CFA color values between the pixel of interest and its neighbors. Two mechanisms are employed for the accurate estimation: distance-related and edge-sensing weighting to reflect the confidence levels of the inter-pixel chrominance components, and pseudoinverse-based estimation from the components in a window. Then from the acquired CFA color component and two estimated chrominance components, the three primary colors are reconstructed by a linear color transform, which is optimized for the least transform error. Our experiments show that the proposed method is much better than other published universal demosaicking methods.

Network coding (NC) brings substantial improvements in terms of throughput and delay in collaborative media streaming applications. A key aspect of NC-driven live peer-to-peer streaming is the packet scheduling policy. Indeed, lack of synchronization among peers usually results in significantly redundant packet transmission, which in turn leads to severe bandwidth inefficiencies. In this paper, we address the problem of finding a suitable asynchronous packet scheduling policy that greatly helps to overcome this critical redundant transmission problem. We propose a bandwidth cost minimization technique under a full video packet recovery constraint. In order to add scalability and improved performance, we also further derive a distributed packet scheduling algorithm. Both implementation and analytical considerations of the proposed approaches are described in this paper. Experimental results confirm that the proposed algorithms deliver higher bandwidth efficiency with reduced redundancy and communication overhead rate and, consequently, better quality-of-service in terms of improved video quality and delivery ratio.

This paper proposed a distributed packet scheduling algorithm for live peer-to-peer streaming system, where network coding is extended to improve the efficiency in bandwidth utilization. A problem of superfluous packet transmission due to the lack of synchronization among peers is identified. This problem often leads to bandwidth inefficiencies. We solve the problem of finding a suitable asynchronous packet scheduling policy by posing and solving a bandwidth allocation problem. This proposed scheduling policy can reduce the superfluous packet transmission, thereby achieving a more efficient bandwidth usage and an improved quality of service in live streaming applications. Experimental results confirm that the proposed scheme demonstrates significantly better video quality, delivery ratio under different network size and different loss rate compared with other push-based schemes.

The authors consider the problem of reconstructing a signal from the outputs of several systems, which are sampled with periods related by rational factors. They present a unified framework in terms of which generalised sampling theories presented by previous researchers can be discussed. Using the spectral aliasing matrix (SAM), the modelling of generalised sampling can be studied in the cases of both bandlimited and non‐bandlimited input signals. In comparison with the generalised sampling theories by Papoulis, Brown and some other scientists, which were based on the same sampling rate, they extend the theory by introducing the SAM and formulating sampling in an elegant matrix representation and considering generalised sampling of different sampling rates with rational factors. Several solution methods of the derived system of linear equations are presented. These methods can also be applied in both the band‐limited and the non‐band‐limited input signal cases, and the easiest reconstruction implementation can be chosen in the temporal or the frequency domain. Finally, they present some experimental results with a one‐dimensional signal designed to test the performance of the proposed algorithm for various sampling rates and under different noise conditions.

This paper proposes a new push-based peer-to-peer (P2P) communication method to optimally transmit scalable video packets over lossy networks. In our scheme, a rate allocation optimization is performed at the sender node. Different from previous sender/receiver driven schemes, our scheme does not need to update buffer maps or request packets periodically, which reduces the amount of redundant packets and yields to less traffic of video data over the Internet. The proposed optimized rate allocation algorithm calculates in advance the number of needed packets according to the cumulative uplink rate between senders and the receiver, and the loss rate of the link. After calculation, each sender sends the hierarchical network coded packets to receivers based on the estimated bandwidth and resource share allocation. With this method, the waste of bandwidth, and the delay caused by the communications among peers can both be reduced.

Vector quantisation provides better rate‐distortion performance over scalar quantisation even for a random vector with independent dimensions. However, the design and implementation complexity of vector quantisers is much higher than that of scalar quantisers. To reduce the complexity while achieving performance close to optimal vector quantisation or better than scalar quantisation, the authors propose a new quantisation scheme, which consists of transformation and scalar quantisation. The transformation is to decorrelate and raise the dimensionality of the input data, for example, to convert a two‐axis representation in two‐dimensional into a tri‐axis representation; then scalar quantisation is applied to each of the raised dimensions, for example, along three axes. The proposed quantiser is asymptotically optimal/suboptimal for low/high rate quantisation, especially for the quantisation with certain prime number of quantisation levels. The proposed quantiser has O ( N 2 ) design complexity, whereas the design complexity of VQ is O ( N !), where N is the number of quantisation levels per dimension. The experimental results show that the average bit‐rate achieves 0.4–24.5% lower than restricted/unrestricted polar quantisers and rectangular quantisers for signals of circular and elliptical Gaussian and Laplace distributions. It holds the potential of improving the performance of the existing image and video coding schemes.

Robust head-shoulder detection has widely been utilized in video surveillance applications. However, most state-of-the-art approaches are very time-consuming and unable to effectively handle videos with radical pose and viewpoint changes. In this paper, we introduce a robust and rapid head-shoulder detection method, which is invariant to pose and viewpoint for video surveillance. The proposed method combines an attention-based foreground segmentation module and a multiview head-shoulder detection cascade to achieve high performance in both accuracy and speed. Specifically, the attention-based foreground segmentation module firstly detects not only active regions that involve motions but also static areas where people stand or sit still in video frames. These regions are then provided to a two-layer head-shoulder detection cascade which is composed of a preliminary linear classifier which eliminates most obvious non-head-shoulder windows rapidly, and a set of histogram intersection kernel (HIK) based multi-view classifiers that can precisely detect head-shoulders with different pose and viewpoint. When compared to the leading methods in the challenging PETS 2009 benchmark dataset, our approach obtains highly competitive results in terms of effectiveness and efficiency.

We propose a non-parametric hierarchical event model to perform online anomaly detection in videos. A dynamic exemplar set is first used to represent observed event samples which updates itself every time when a new sample comes in. Upon this set, clusters are extracted to summarize the exemplars, offering a compact yet informative data structure for past event samples. Abnormal events are detected by both considering their dissimilarity with the model and low frequency. Experiments on real world crowd surveillance videos demonstrate the effectiveness and robustness of the proposed algorithm which shows reliable detection rates and low false alarms.

For high light sensitivity, new CFA designs use panchromatic pixels, aka white pixels, that no visible spectrum energy is filtered. Kodak's CFA2.0 has 50% white pixels, but the demosaicking performance is not good. We present in this work a set of new color filter arrays (CFA) of high light sensitivity and high demosaicking performance which were obtained by using a CFA design methodology in the frequency domain. The new patterns are of size 5×5 and come from the same frequency structure, which has one luma in the base band at (0, 0) and four chromas (two conjugate pairs) placed at (4π/5, 2π/5), (- 4π/5, - 2π/5), (2π/5, - 4π/5) and (- 2π/5, 4π/5), respectively. The new patterns are optimized to have only white (panchromatic) and three primary color pixels and the pixels are found to be 40% white, 20% red, 20% green and 20% blue by pixel color constrained optimization. Our demosaicking experiments show that our new CFA patterns outperform Kodak CFA2.0 in both objective and subjective quality.

Cluster analysis is a tool for data analysis. It is a method for finding clusters of a data set with most similarity in the same group and most dissimilarity between different groups. In general, there are two ways, mixture distributions and classifi

A color filter array (CFA) used in a digital camera is a mosaic of spectrally selective filters, which allows only one color component to be sensed at each pixel. The missing two components of each pixel have to be estimated by methods known as demosaicking. The demosaicking algorithm and the CFA design are crucial for the quality of the output images. In this paper, we present a CFA design methodology in the frequency domain. The frequency structure, which is shown to be just the symbolic DFT of the CFA pattern (one period of the CFA), is introduced to represent images sampled with any rectangular CFAs in the frequency domain. Based on the frequency structure, the CFA design involves the solution of a constrained optimization problem that aims at minimizing the demosaicking error. To decrease the number of parameters and speed up the parameter searching, the optimization problem is reformulated as the selection of geometric points on the boundary of a convex polygon or the surface of a convex polyhedron. Using our methodology, several new CFA patterns are found, which outperform the currently commercialized and published ones. Experiments demonstrate the effectiveness of our CFA design methodology and the superiority of our new CFA patterns.

In this paper, we propose a new blind video watermark detection method which is based on 3D-DWT transform. We find that the coefficients in the high frequency band of temporal wavelet transform (TWT) are nearly orthogonal to the normally-distributed watermark, which is the basis of our proposed extraction method. The watermarking procedure is similar to the former 3D-DWT method, first 2D-DWT in the spatial domain, then 1D-DWT in the temporal domain (TWT). The difference is that we do not use the low frequency band in the 1D-DWT procedure, for the property of the TWT high frequency band that we have found in the detection procedure. We also propose to use two zero-mean normally-distributed watermarks in embedding to avoid block effects. Finally, an absolute value detection scheme is proposed. Using this scheme our scheme can resist frame dropping attack and temporal frame averaging attack, because the TWT high frequency band reflects the changing parts along the temporal axis.

Detecting abnormal behaviors in crowd scenes is quite important for public security and has been paid more and more attentions. Most previous methods use offline trained model to perform detection which can't handle the constantly changing crowd environment. In this paper, we propose a novel unsupervised algorithm to detect abnormal behavior patterns in crowd scenes with online learning. The crowd behavior pattern is extracted from the local spatio-temporal volume which consists of multiple motion patterns in temporal order. An online self-organizing map (SOM) is used to model the large number of behavior patterns in crowd. Each neuron can be updated by incrementally learning the new observations. To demonstrate the effectiveness of our proposed method, we have performed experiments on real-world crowd scenes. The online learning can efficiently reduce the false alarms while still be able to detect most of the anomalies.

For current fingerprint indexing schemes, global textures and minutiae structures are usually utilized. To extend the existing methods of feature extraction, we study the three most popular local descriptors, SIFT, SURF and DAISY, for fingerprint indexing and give a comparison of indexing performance for evaluation of these three features on public fingerprint databases. For index construction, the locality-sensitive hashing (LSH) is used to efficiently retrieve similarity queries in a small fraction of the database. Experiments show that SURF and DAISY are applicable for fingerprint indexing as SURF features perform equally well or better than SIFT features while DAISY improves not so significantly.

The manifold learning algorithms are promising data analysis tools. However, to fit an unseen point in a learned model, the point must be located in the training set, which limits its scalability. In this paper, we discuss how to select landmarks from the data to help locate the test points. Our method is for data on manifolds: the way the landmarks represent the data in the ambient space should resemble the way they represent the data on the manifold. Compared to the previous research, (i) Our test foregoes the requirement of knowing the intrinsic manifold dimension and thus is more applicable and robust. (ii) Our selection implies a provable topology preservation property. (iii) We also provide a way to improve existing landmarks. Experiments on the synthetic data and the real data have been done. The results support the proposed properties and algorithms. 1

In this paper, we propose a learning-based method for adding colours to grayscale images. In contrast to many previous computer-aided colourizing methods, which require intensive and accurate human intervention, our method needs only the user to provide a colourful image of the similar content as the grayscale image. We accept the “image manifold ” assumption and apply manifold learning methods to model the relations between the chromatic channels and the gray levels in the training images. Then we synthesize the objective chromatic channels using the learned relations. Experiments show that our method gives superior results to those of the previous work. 1

This paper addresses the face hallucination problem of converting thermal infrared face images into photo-realistic ones. It is a challenging task because the two modalities are of dramatical difference, which makes many developed linear models inapplicable. We propose a learning-based framework synthesizing the normal face from the infrared input. Compared to the previous work, we further exploit the local linearity in not only the image spatial domain but also the image manifolds. We have also developed a measurement of the variance between an input and its prediction, thus we can apply the Markov random field model to the predicted normal face to improve the hallucination result. Experimental results show the advantage of our algorithm over the existing methods. Our algorithm can be readily generalized to solve other multi-modal image conversion problems as well.

Most of current fingerprint indexing schemes utilize features based on global textures and minutiae structures. To extend the existing technology of feature extraction, this paper proposes a new fingerprint indexing and retrieval scheme using scale invariant feature transformation (SIFT), which has been widely used in generic image retrieval. With slight loss in effectiveness, we reduce the number of features generated from one fingerprint for efficiency. To cope with the uncertainty of acquisition (e.g. partialness, distortion), we use a composite set of features to form multiple impressions for the fingerprint representation. In the index construction phase, the use of locality-sensitive hashing (LSH) allows us to perform similarity queries by only examining a small fraction of the database. Experiments on database FVC2000 and FVC2002 show the effectiveness of our proposed scheme.

Manifold learning methods are promising data analysis tools. However, if we locate a new test sample on the manifold, we have to find its embedding by making use of the learned embedded representation of the training samples. This process often involves accessing considerable volume of data for large sample set. In this paper, an approach of selecting "landmark points" from the given samples is proposed for hierarchical structuring of data on manifolds. The selection is made such that if one use the Voronoi diagram generated by the landmark points in the ambient space to partition the embeded manifold, the topology of the manifold is preserved. The landmark points then are used to recursively construct a hierarchical structure of the data. Thus it can speed up queries in a manifold data set. It is a general framework that can fit any manifold learning algorithm as long as its result of an input can be predicted by the results of the neighbor inputs. Compared to the existing techniques of organizing data based on spatial partitioning, our method preserves the topology of the latent space of the data. Different from manifold learning algorithms that use landmark points to reduce complexity, our approach is designed for fast retrieval of samples. It may find its way in high dimensional data analysis such as indexing, clustering, and progressive compression. More importantly, it extends the manifold learning methods to applications in which they were previously considered to be not fast enough. Our algorithm is stable and fast, and its validity is proved mathematically.

In this paper, we present a progressive reversible data hiding technique by symmetrical histogram expansion in the transform domain of piecewise-linear Haar (PLHaar). Data are embedded into the PLHaar coefficients of images progressively from the pivotal bin of a histogram of PLHaar coefficients to both sides of the pivot symmetrically. With PLHaar, no overflow or underflow occurs to the pixel values, and our data hiding method achieves the highest embedding capacity with PSNR around 50 dB compared to the previous methods in the literature. The method can also be applied to artificial images with an exactly flat histogram, which is impossible for a method to hide data in the spatial domain. The progressiveness of the proposed method also enables a rough but automatic capacity-PSNR control. The effectiveness of our method is demonstrated with a number of experiments.

Symmetric phase-only filter (SPOF) has been widely applied to image registration and recognition, and has been proved efficient for fingerprint verification. Fingerprint images have a characteristic that the dominant information is concentrated in an elliptic frequency band of their ridges in low frequency domain. However, the existent fingerprint verification methods based on SPOF do not take this characteristic into account. To improve the performance of SPOF for fingerprint recognition, an appropriate region of support (ROS) can be used to set the least significant frequency region to zero. By means of theoretical and experimental analysis, we have found the optimal ROS for SPOF that achieves the best discrimination power among all the possible ROSs. Experiments show that our optimal ROS-based SPOF is more efficient than the method of band-limited SPOF (BLPOC).

In this paper, we propose a novel transform that preserves the dynamic range-infinity-norm rotation. This transform is perfectly reversible and piecewise linear, and keeps the maximum value unchanged. We apply the transform to reversible data hiding, which can be utilized for fragile data hiding and covert communication. After the inverse transform applied to the image with hidden data, no overflow or underflow occurs to the pixel values, and small changes of the coefficients in the transform domain result in small changes of the corresponding pixel values after reconstruction. With progressive symmetrical histogram expansion, we obtain very high embedding capacity for data hiding in the transform domain of infinity-norm rotation. Furthermore, the embedding capacity can be further expanded with blocked coefficients and low-frequency coefficients, which is demonstrated by our experiments.

Two principles to produce new possibilities for the radix-2 Discrete Cosine Transform (DCT) have been presented in this paper. One is to employ matrix factorization through revealing the intrinsic relationship among several existing famous algorithms, which is regarded as an effective guide for exploring new algorithms. The other is to make use of the orthogonal property of the DCT matrix. As long as the recursive kernel of an algorithm is orthogonal, there must be a twin fast DCT algorithm of it. Matrix factorization is applied through the research and can be used to show how data flows and compute the computational complexity easily. At the end of this paper, we also present a new fast algorithm for DCT. It enjoys the parallel structure which is simpler for programming and hardware implementation and keeps the same numbers of the additions and multiplications as the fastest algorithms.

Fingerprint indexing is an efficient technique that greatly improves the performance of fingerprint based person authentication systems by reducing the number of comparison. In this paper, we propose an indexing method based on fingerprint registration with a novel feature called local axial symmetry (LAS). The location and direction estimation of reference point are achieved in a straightforward way after the LAS field is achieved. Then the registered orientation field is utilized as a feature vector to perform the following indexing. A new scheme of the experiment is introduced and satisfactory experimental results are achieved on FVC2000 DB2 that the average search space is only 2.34% of all fingers in the condition of equal-sized training set and testing set.

Although an all-in-focus image can be generated by mosaicking all the best focused regions of a sequence of acquired images taken under different focal settings, an even better-resolved result can be obtained with those discarded less-focused regions which actually contain more useful information. This paper presents a method to estimate an all-in-focus and super-resolved image of a scene from a sequence of such images. The proposed method applies mosaicking and super-resolution techniques simultaneously to iteratively reconstruct a better image by blind trellis searching. Partitioning images into small blocks makes the new method parallel and fast over blocks. With our regularized approach, a high resolution image can be efficiently estimated both in the underdetermined case and in the case with registration errors.

In this paper, a matrix factorization method is presented for reversible integer M-band wavelet transforms. Based on an algebraic construction of orthonormal M-band wavelets with perfect reconstruction, the polyphase matrix can be factorized into a finite sequence of elementary reversible matrices that map integers to integers reversibly. We show that the reversible integer mapping is essentially equivalent to the lifting scheme, thus we extend the classical lifting scheme to a more flexible framework.

A new compound image compression algorithm is proposed, based on shape primitive extraction and coding (SPEC). The SPEC first segments a compound image into text/graphics pixels and pictorial pixels, by extracting the shape primitives of text/graphics. Then all the shape primitives are losslessly compressed with a combined shape-based and palette-based coding algorithm. The remaining pictorial pixels are coded with JPEG2000. Experimental results show that the SPEC has very low complexity and provides visually lossless quality, while yielding competitive compression ratios.

Fingerprint indexing is an efficient technique that greatly improves the performance of automated fingerprint identification systems. We propose a continuous fingerprint indexing method based on location, direction estimation and correlation of fingerprint singular points. Location and direction estimation are achieved simultaneously by applying a T-shape model to directional field of fingerprint images. The T-shape model analyzes homocentric sectors around the candidate singular points to find lateral-axes and further main-axes. Then a distortion-tolerant filter of minimum average correlation energy is utilized to obtain a correlation-based similarity measure which gives the evidence of searching priority. The experiment is performed by 400-fingerprint retrieval from 10,000 templates and the mean search space is only 3.46% of the whole dataset.

Owing to its effectiveness and simplicity, intensity-based method works well for registration of images. However, it needs a large amount of computation for geometric transformation. In this paper, we present two shear-resize matrix factorizations to accelerate the transformation. A transform matrix can be factorized into two shears and a fixed non-uniform resize, or three shears and a customizable resize. A customizable resize can be uniform in all dimensions or scaling just in one dimension. Shears can be implemented very fast by memory-shift, and a resize can be done by simple axis-aligned interpolation. The factorizations can be applied to both rigid-body and affine transformations. Their efficiency is performed by experiments on some standard test images and fingerprint images. The methods are quite promising for hardware implementation, and can also be extended to 3D or higher dimensional fast geometric transformation.

We present a compound image compression algorithm for real-time applications of computer screen image transmission. It is called shape primitive extraction and coding (SPEC). Real-time image transmission requires that the compression algorithm should not only achieve high compression ratio, but also have low complexity and provide excellent visual quality. SPEC first segments a compound image into text/graphics pixels and pictorial pixels, and then compresses the text/graphics pixels with a new lossless coding algorithm and the pictorial pixels with the standard lossy JPEG, respectively. The segmentation first classifies image blocks into picture and text/graphics blocks by thresholding the number of colors of each block, then extracts shape primitives of text/graphics from picture blocks. Dynamic color palette that tracks recent text/graphics colors is used to separate small shape primitives of text/graphics from pictorial pixels. Shape primitives are also extracted from text/graphics blocks. All shape primitives from both block types are losslessly compressed by using a combined shape-based and palette-based coding algorithm. Then, the losslessly coded bitstream is fed into a LZW coder. Experimental results show that the SPEC has very low complexity and provides visually lossless quality while keeping competitive compression ratios.

In the above-mentioned paper, Wei and Fahn proposed a neural architecture, the multisynapse neural network, to solve constrained optimization problems including high-order, logarithmic, and sinusoidal forms, etc. As one of its main applications, a fuzzy bidirectional associative clustering network (FBACN) was proposed for fuzzy-partition clustering according to the objective-functional method. The connection between the objective-functional-based fuzzy c-partition algorithms and FBACN is the Lagrange multiplier approach. Unfortunately, the Lagrange multiplier approach was incorrectly applied so that FBACN does not equivalently minimize its corresponding constrained objective-function. Additionally, Wei and Fahn adopted traditional definition of fuzzy c-partition, which is not satisfied by FBACN. Therefore, FBACN can not solve constrained optimization problems, either.

Intensity-based methods work well for multimodal image registration owing to their effectiveness and simplicity, but the computation for geometric transforms is a heavy load. To accelerate the transformation, in this paper, we present two shear-resize matrix factorizations for general 3D linear transformation, to factorize a transform matrix into three shears and a fixed resize, or four shears and a customizable resize. Shears can be implemented very fast by memory shift and a resize can be done by axis-aligned resampling. The factorizations can be applied to both rigid-body and affine transformations. The experiments on MRI and CT volumes show that our method is 10 times faster than the naive transformation. The method is quite promising for hardware and parallel implementation, and is also valid for monomodal image registration.

The tool for video analysis addressed in this paper is called co-histogram, which is a statistic graph generated by counting the corresponding pixel pairs of two images. A co-histogram shows how the pixels are distributed among combinations of two image pixel values. By means of the co-histogram, we can have a visual perception of a widely used metric, peak signal-to-noise ratio (PSNR), and the symmetry (or weighted symmetry) of a co-histogram is also good for video shot boundary detection and object entering-exiting detection. Co-histogram symmetry is insensitive to camera zooming, which is good for reducing false detection. Co-histogram symmetry is also easily computable and directly intercomparable

In this paper, we present a novel hybrid image coding scheme for real-time applications of computer screen video transmission. Based on the Mixed Raster Content (MRC) multilayer imaging model, the background picture is compressed with lossy JPEG, and the foreground layer consisting of text and graphics is compressed with a block-based lossless coding algorithm, which integrates shape-based coding, palette-based coding, palette reuse, and LZW algorithm. The key technique is to extract text and graphics from background pictures accurately and with low complexity. Shape primitives, such as lines, rectangles, and isolated pixels with prominent colors, are found to be significant clues for textual and graphical contents. The shape-based coding in our lossless algorithm provides intelligence to extract the computer-generated text and graphics elegantly and easily. Experimental results demonstrate the efficiency and low complexity of our proposed hybrid image coding scheme.

In this paper, we presented a method for integer reversible implementation of KLT for multiple component image compression. The progressive-to-lossless compression algorithm employed the JPEG-2000 transform coding strategy using the multiple component transform (MCT) across the components, followed by a 2-dimensional wavelet transform on individual eigen images. The linear MCTs we tested and compared are KLT, discrete wavelet transform (DWT), and a tasselled cap transform (TCT) for TM satellite images only. The computational complexity of the reversible integer implementation is no more than that of naive transformation, and the overhead data is very small. Its effectiveness was evaluated using two 6-band landsat TM satellite images and an 80-component hyper-spectral remotely-sensed image. Experiments with KLT and wavelet based JPEG-2000 show that reversible KLT (RKLT) outperforms other approaches for all of the test images in the case of both lossy and lossless compression.

Peak signal-to-noise ratio (PSNR) has found its application as an evaluation metric for image coding, but in many instances it provides an inaccurate representation of the image quality. The new tool proposed in this paper is called co-histogram, which is a statistic graph generated by counting the corresponding pixel pairs of two images. For image coding evaluation, the two images are the original image and a compressed and recovered image. The graph is a two-dimensional joint probability distribution of the two images. A co-histogram shows how the pixels are distributed among combinations of two image pixel values. By means of co-histogram, we can have a visual interpretation of PSNR, and the symmetry of a co-histogram is also significant for objective evaluation of remote sensing image compression. Our experiments with two SAR images and a TM image using DCT-based JPEG and wavelet-based SPIHT coding methods perform the importance of the co-histogram symmetry.

A color transform is necessary for better color image coding. In this paper, we concentrate on a comparative study of color transforms for color image coding in order to find the best one among 11 published color transforms: YCrCb, NTSC, PAL, HDTV, WW, XYZ, DCT, DHT, two approximate K-L transforms (K1K2K3 and KLT) and the original reversible color transform (ORCT) adopted in JPEG-2000. Experiment results with JPEG-2000 verification model (VM5.1) are sorted by color transforms and counted up for all the test images and diverse bit rates. The sorting scoring table shows that the discrete cosine transform performs best among 11 tested color transform at 6 lossy bit rates for all the 2 JPEG-2000 color test images and other 23 color images we used. Then we derive an integer reversible transform of DCT and an approximate implementation using additions and shifts only for both lossless and lossy color image coding. Experiments with the integer reversible color transforms show that the proposed transform scheme is better than ORCT for lossy image coding.