DVC based video codecs proposed in the literature generally include a reverse (feedback) channel between the encoder and the decoder. This channel is used to communicate the dynamic parity bit request messages from the decoder to the encoder resulting in an optimum dynamic variable rate control implementation. However it is observed that this dynamic feedback mechanism is a practical hindrance in a number of practical consumer electronics applications. In this paper we proposed a novel transform domain Unidirectional Distributed Video Codec (UDVC) without a feedback channel. First, all Wyner-Ziv frames are divided into rectangular macroblocks. A simple metric is used for each block to represent the correlations between the corresponding blocks in the adjacent key frame and the Wyner-Ziv frame. Based on the value of this metric, parity is allocated dynamically for each block. These parities are either stored for offline processing or transmitted to the DVC decoder for on line processing. Simulation results show that the proposed codec outperforms the existing UDVC solutions by a significant margin.

Contourlet transform (CT) is a new image representation method, which can efficiently represent contours and textures in images. However, CT is a kind of overcomplete transform with a redundancy factor of 4/3. If it is applied to image compression straightforwardly, the encoding bit-rate may increase to meet a given distortion. This fact baffles the coding community to develop CT-based image compression techniques with satisfactory performance. In this paper, we analyze the distribution of significant contourlet coefficients in different subbands and propose a new contourlet-based embedded image coding (CEIC) scheme on low bit-rate. The well-known wavelet-based embedded image coding (WEIC) algorithms such as EZW, SPIHT and SPECK can be easily integrated into the proposed scheme by constructing a virtual low frequency subband, modifying the coding framework of WEIC algorithms according to the structure of contourlet coefficients, and adopting a high-efficiency significant coefficient scanning scheme for CEIC scheme. The proposed CEIC scheme can provide an embedded bit-stream, which is desirable in heterogeneous networks. Our experiments demonstrate that the proposed scheme can achieve the better compression performance on low bit-rate. Furthermore, thanks to the contourlet adopted in the proposed scheme, more contours and textures in the coded images are preserved to ensure the superior subjective quality.

In this paper, we investigate minimum mean absolute error (mmae) predictors for lossless image coding. In some prediction-based lossless image coding systems, coding performance depends largely on the efficiency of predictors. In this case, minimum mean square error (mmse) predictors are often used. Generally speaking, these predictors have a problem that outliers departing very far from a regression line are conspicuous enough to obscure inliers. That is, in image compression, large prediction errors near edges cause the degradation of the prediction accuracy of flat areas. On the other hand, mmae predictors are less sensitive to edges and provide more accurate prediction for flat areas than mmse predictors. At the same time, the prediction accuracy of edge areas is brought down. However, the entropy of the prediction errors based on mmae predictors is reduced compared with that of mmse predictors because general images mainly consist of flat areas. In this study, we adopt the Laplacian and the Gaussian function models for prediction errors based on mmae and mmse predictors, respectively, and show that mmae predictors outperform conventional mmse-based predictors including weighted mmse predictors in terms of coding performance.

In video compression, the information transmitted from the encoder to the decoder can be classified into two categories: side information, which carries action instructions to be performed, and data such as the residual error of the texture. As video compression technology has matured, better compression has been achieved by increasing the ratio of side information to data, while reducing the overall bit rate. However, there is a limit to this method because the side information becomes a significant fraction of the overall bit rate. In recent video compression technologies, the decoder tends to share the burden of the decision making in order to achieve a higher compression ratio. To further improve the coding efficiency, we tried to provide the decoder with a more active role in reducing the amount of data. According to this approach, by using reconstructed pixels that surround a target block to produce a better sample predictor of the target block, the amount of side information and the residual error of the texture are reduced. Furthermore, multiple candidates of the sample predictor are utilized to create a better sample predictor without increasing the amount of side information. In this paper, we employ a template matching method that makes the decoder more active. The template matching method is applied to the conventional video codec to improve the prediction performance of intra, inter, and bi-directional pictures in video. The results show that improvements in coding efficiency up to 5.8% are achieved.

An encryption scheme is proposed that considers hierarchies in media, such as text, images, sound, and so on, in a composite multimedia content to enable versatile access control. In the proposed scheme, a content provider has only one managed key (the master key) for a particular composite multimedia content, and an user who is permitted to access a reserved content entities in the composite content receives only one key that is subordinately generated from the master key. Another key generated from the identical master key is delivered to another user, and this permits the user to access different entities. This scheme introduces a new key concept, namely "unusable key," to keep all entities encrypted in a particular medium and to simultaneously decrypt several entities in other media. The other new key, "numbering key," is also used in this scheme to support simultaneous partial decryption of multiple images that are coded with a scalable coding technology. Simulation results show the effectiveness of the proposed scheme; in particular, the length of the managed master key and that of keys to be delivered to users are small.

It is well-known that one of the best wavelet-based image compression techniques, called Wavelet Difference Reduction-WDR, has both simple algorithm and comparative rate-distortion results comparing to other coders in the literature. In this paper, we propose an algorithm to enhance the performance of WDR coder in a very efficient way. The methodology is highly based on the context adaptive model. High-order statistical context modeling is used for significant coefficients prediction by scanning order adaptation of WDR. In binary mode, the coder is clearly better than Set Partitioning in Hierarchical Trees (SPIHT). A new conditional Arithmetic Coding (AC) using array of contexts is specifically designed for WDR-based coder. Based on our simulation results of all images in the test set which covers various types of images, it is apparent that our proposed coder in AC mode generates the rate-distortion results that are superior to those of all other WDR-based coders in the literature. Furthermore, it is very competitive to SPIHT-AC and JPEG2000, the best coders, at all bit rates.

Set Partitioning in Hierarchical Trees (SPIHT) is a highly efficient technique for compressing Discrete Wavelet Transform (DWT) decomposed images. Though its compression efficiency is a little less famous than Embedded Block Coding with Optimized Truncation (EBCOT) adopted by JPEG2000, SPIHT has a straight forward coding procedure and requires no tables. These make SPIHT a more appropriate algorithm for lower cost hardware implementation. In this paper, a modified SPIHT algorithm is presented. The modifications include a simplification of coefficient scanning process, a 1-D addressing method instead of the original 2-D arrangement of wavelet coefficients, and a fixed memory allocation for the data lists instead of a dynamic allocation approach required in the original SPIHT. Although the distortion is slightly increased, it facilitates an extremely fast throughput and easier hardware implementation. The VLSI implementation demonstrates that the proposed design can encode a CIF (352288) 4:2:0 image sequence with at least 30 frames per second at 100-MHz working frequency.

JPEG2000, an international standard for still image compression, offers 1) high coding performance, 2) unified lossless/lossy compression, and 3) resolution and SNR scalability. Resolution scalability is an especially promising attribute given the popularity of Super High Definition (SHD) images like digital-cinema. Unfortunately, its current implementation of resolution scalability is restricted to powers of two. In this paper, we introduce non-octave scalable coding (NSC) based on the use of filter banks. Two types of non-octave scalable coding are implemented. One is based on a DCT filter bank and the other uses wavelet transform. The latter is compatible with JPEG2000 Part2. By using the proposed algorithm, images with rational scale resolutions can be decoded from a compressed bit stream. Experiments on digital cinema test material show the effectiveness of the proposed algorithm.

The lifting scheme is an efficient and flexible method for the construction of linear and nonlinear wavelet transforms. In this paper, a novel lossless image coding technique based on the lifting scheme using discrete-time cellular neural networks (DT-CNNs) is proposed. In our proposed method, the image is interpolated by using the nonlinear interpolative dynamics of DT-CNN, and since the output function of DT-CNN works as a multi-level quantization function, our method composes the integer lifting scheme for lossless image coding. Moreover, the nonlinear interpolative dynamics by A-template is used effectively compared with conventional CNN image coding methods using only B-template. The experimental results show a better coding performance compared with the conventional lifting methods using linear filters.

In this paper, we propose a fast fractal image coding based on LMSE (least mean square error) analysis and subblock feature. The proposed method focuses on efficient search of contrast scaling, position of its matched domain block, and isometric transform for a range block. The contrast scaling and the domain block position are searched using a cost function that comes from the LMSE analysis of the range block and its fractal-approximated block. The isometric transform is searched using 2 2 blocks formed with the averages of subblocks of range block and domain block. Experimental results show that the encoding time of a conventional fractal image coding with our search method is 25.6-39.7 times faster than that with full search method at the same bit rate while giving PSNR decrement of 0.2-0.7 dB with negligible deterioration in subjective quality. It is also shown that the encoding time of a conventional fractal image coding with our search method is 3.4-4.2 times faster than Jacquin's fractal image coding and is superior by maximum 0.8 dB in PSNR. It also yields reconstructed images of better quality.

In this paper, we present a design and implementation of the M-channel linear-phase filter banks with unequal-length and same center of symmetry. The filter banks are separated into paraunitary and biorthogonal case. We discuss both cases. A novel filter bank can be regarded as a special class of generalized lapped transform with arbitrary number of channels M. In image coding applications, long basis functions should be used to avoid the blocking artifacts in low-frequency bands, while short basis functions should be used to reduce the ringing artifacts in high-frequency bands. Having the same center of symmetry is suitable for progressive image coder [SPIHT]. Filter banks with such characteristics can be achieved structurally by taking acount of the lattice structure. Finally, several design and image coding examples are shown.

The efficient distribution of stored information has become a major concern in the Internet. Since the web workload characteristics show that more than 60% of network traffic is caused by image documents, how to efficiently distribute image documents from servers to end clients is an important issue. Proxy cache is an efficient solution to reduce network traffic. And it has been shown that an image caching method (Graceful Caching) based on hierarchical coding format performs better than conventional caching schemes in recent years. However, as the capacity of the cache is limited, how to efficiently allocate the cache memory to achieve a minimum expected delay time is still a problem to be resolved. This paper presents an integrated caching algorithm to deal with the above problem for image databases, web browsers, proxies and other similar applications in the Internet. By analyzing the web request distribution of the Graceful Caching, both replacing and pre-fetching algorithms are proposed. We also show that our proposal can be carried out based on information readily available in the proxy server; it flexibly adapts its parameters to the hit rates and access pattern of users' requesting documents in the Graceful Caching. Finally we verify the performance of this algorithm by simulations.

A novel class of time-varying subband transforms and its application in image coding are introduced. We construct the framework in which an analysis subband transform is chosen from a given set of analysis transforms and perfect reconstruction is achieved. To this end, we suggest the reconstruction method from the transformed coefficients by applying the theory of convex projections (POCS). We introduce convex sets for perfect reconstruction from the transformed vector. We further propose other convex sets which assure smoothness of plane regions for image coding applications. We show several image coding examples. The proposed coding method is an extension of conventional one with multiple block-based transforms. At each time instance (block), the transform to be applied is chosen from a given set of orthogonal subband transforms according to a certain criterion. Experimental results show that the use of multiple subband transforms leads to the improvement of coding performance compared to the use of single transform even though there exists side information.

This Letter presents a fast codeword search algorithm based on ordered Hadamard transform. Before encoding, the ordered Hadamard transform is performed offline on all codewords. During the encoding process, the ordered Hadamard transform is first performed on the input vector, and then a new inequality based on characteristic values of transformed vectors is used to reject the unlikely transformed codewords. Experimental results show that the algorithm outperforms many newly presented algorithms in the case of high dimensionality, especially for high-detail images.

In this paper, we analyze wavelet-based coding in a rate-distortion (R-D) sense by using Laplacian and Markov models and verify the results with the performance of the typical embedded coders, EZW and SPIHT, and the non-embedded coder implemented here. Laplacian represents the probability density function (pdf) of wavelet coefficients and Markov statistical dependency within and among subbands. The models allow us to easily understand the behavior of a thresholding and quantization part and a lossless coding part and associate the embedded coders with the nonembedded coder, which is the point the paper approaches. The analytic results are shown to coincide well with the actual coding results.

A fast nearest neighbor codeword search algorithm for vector quantization (VQ) is introduced. The algorithm uses three significant features of a vector, that is, the mean, the variance and the norm, to reduce the search space. It saves a great deal of computational time while introducing no more memory units than the equal-average equal-variance codeword search algorithm. With two extra elimination criteria based on the mean and the variance, the proposed algorithm is also more efficient than so-called norm-ordered search algorithm. Experimental results confirm the effectiveness of the proposed algorithm.

In this paper, we develop a fast dynamic programming technique to construct the subband decomposition yielding the maximum coding gain. We first derive a unified coding gain applicable to arbitrary subband decompositions and arbitrary filter bases. Then, we prove that the unified coding gains are monotonically increasing function for the implementation complexities. Based on this phenomenon, we treat the implementation complexity and the coding gain in the same way as the rate and distortion function. Finally, we applied the developed algorithm to the wavelet packet based image coding, so as to verify coding performance improvement by the proposed algorithm.

In fractal image coding, for each range block, the best matching domain block is identified, and information from the best matching domains and range blocks are transmitted to the decoder for image reconstruction. In this paper, the similarity between range blocks and domain blocks is evaluated according to their centers of gravity. The number of searched domain blocks are reduced by limiting the candidates for the best matching domain blocks to those domain blocks whose similarity to the range block are high. Using simulation experiments, the number of candidates for the best matching domain blocks were reduced to about 10-23% of the current method. Thus, our proposed method had significantly reduced the number of searched domain blocks below the current method and at the same time it turns out that degradation of the reconstructed image was seldom observed.

In this paper we treat a novel adaptation strength according to neighborhood ranking of self-organizing neural networks with the objective of avoiding the initial dependency of reference vectors, which is related to the strength in the neural-gas network suggested by Martinetz et al. The present approach exhibits the effectiveness in the average distortion compared to the conventional technique through numerical experiments. Furthermore the present approach is applied to image data and the validity in employing as an image coding system is examined.

Uniform color spaces are very important in color engineering, image source coding and multimedia information processing. In spite of many efforts have been paid on the subject, however, construction of an exact uniform color space seems difficult until now. Existing approaches mainly used local and heuristic approximations. Moreover, there seemed also certain confusion in definitions of the uniform spaces. In this paper we discuss the issue from a point of view of global Riemannian geometry. The equivalence between global and local definitions of uniform space are shown. Then both an exact and a simplified algorithm are presented to uniformize either a part or the totality of a color space. These algorithms can be expected to find applications in optimal quantization of color information.