Image compression with a hybrid wavelet-fractal coder
A hybrid wavelet-fractal coder (WFC) for image compression is proposed. The WFC uses the fractal contractive mapping to predict the wavelet coefficients of the higher resolution from those of the lower resolution and then encode the prediction residue with a bitplane wavelet coder. The fractal prediction is adaptively applied only to regions where the rate saving offered by fractal prediction justifies its overhead. A rate-distortion criterion is derived to evaluate the fractal rate saving and used to select the optimal fractal parameter set for WFC. The superior performance of the WFC is demonstrated with extensive experimental results.

title: The seamlessly multiplexed embedded codec (SMEC) and its application in image coding
abstract: The landmark JPEG 2000 image compression standard offers not only superior compression performance, but also incredible flexibility. The compressed bitstream of JPEG 2000 can be flexibly reorganized to another bitstream of different bitrate, resolution, and spatial region of interest (ROI) or a combination of any of the above. Such flexibility is achieved by multiplexing the compressed bitstream pieces of multiple code-blocks together into a combined bitstream, with the length of the code-block bitstream piece (LOCB) embedded in the combined bitstream. The LOCB serves both to reorganize the bitstream, and to decode the bitstream. It represents a significant overhead, especially since there is no correlation between the neighbor LOCBs. In this work, we introduce seamless multiplexing, and separate the information needed for the reorganization, i.e., the LOCB, from the compressed bitstream itself by using the decoder pointer to multiplex the bitstream pieces. As a result, the compressed bitstream consists of code-block bitstream pieces seamlessly concatenated to each other. With seamless multiplexing, only the compressed bitstream (without LOCB) needs to be delivered to the receiving client. It results in better compression performance and higher granularity of access. Another benefit of seamless multiplexing is that the relative coding orders of the code-blocks are preserved in the bitstream reorganization. As a result, the seamlessly multiplexed embedded codec (SMEC) may utilize the dependencies among the code-blocks in the coding, thus further boost the compression performance.

title: Arbitrarily shaped video-object coding by wavelet
abstract: Video-object coding is one of the most important functionalities proposed by MPEG-4. We propose a new wavelet method to encode the texture of an arbitrarily shaped object, both for the still and for the video-object. The method uses the shape adaptive wavelet transform (SA-DWT) in MPEG-4 still object coding, but with a computationally more efficient lifting implementation. The transformed object coefficients are then quantized and entropy encoded with a partial bit-plane embedded coder, which greatly improves the coding efficiency. We denote the coding algorithm as the video-object wavelet (VOW) coder. Experimental results show that VOW significantly outperforms MPEG-4 in still-object coding, and achieves a comparable performance in video-object coding in terms of PSNR. Moreover, the VOW decoded object looks better subjectively, with less annoying blocking artifacts than that of MPEG-4.

title: Distributed hosting of Web content with erasure coding and unequal weight assignment
abstract: By sharing and hosting personal Web content in a peer-to-peer (P2P) fashion, we may increase the bandwidth of retrieval and improve the reliability of retrieval. The cost is that the Web content has to be replicated to and stored in the peers, consuming valuable network bandwidth and peer storage space. In this work, we develop two technologies, namely hierarchical content organization with unequal weight assignment and erasure coding, to reduce the amount of content to be distributed, yet still maintain the retrieval speed up and reliability. Significant improvement over the ordinary Web server is demonstrated.

title: An embedded still image coder with rate-distortion optimization
abstract: It is well known that the fixed rate coder achieves optimality when all coefficients are coded with the same rate-distortion (R-D) slope. In this paper, we show that the performance of the embedded coder can be optimized in a rate-distortion sense by coding the coefficients with decreasing R-D slope. We denote such a coding strategy as rate-distortion optimized embedding (RDE). RDE allocates the available coding bits first to the coefficient with the steepest R-D slope, i.e., the largest distortion decrease per coding bit. The resultant coding bitstream can be truncated at any point and still maintain an optimal R-D performance. To avoid the overhead of coding order transmission, we use the expected R-D slope, which can be calculated from the coded bits and is available in both the encoder and the decoder. With the probability estimation table of the QM-coder, the calculation of the R-D slope can be just a lookup table operation. Experimental results show that the rate-distortion optimization significantly improves the coding efficiency in a wide bit rate range.

title: On the compression and streaming of concentric mosaic data for free wandering in a realistic environment over the Internet
abstract: In this paper, we describe a system for wandering in a realistic environment over the Internet. The environment is captured by the concentric mosaic, compressed via the reference block coder (RBC), and accessed and delivered over the Internet through the virtual media (Vmedia) access protocol. One of the key contributions of the paper is the proposal of the RBC concentric mosaic coder. The RBC coder not only compresses the huge dataset of the concentric mosaic very efficiently, but also produces a well-organized bitstream that can be accessed just-in-time (JIT). To reconstruct a virtual view, only a portion of the RBC bitstream needs to be accessed and decoded. This greatly reduces the memory and computation requirement of the viewer compared with first decoding the entire concentric mosaic data set and then rendering from the decoded data. Our second contribution is the employment of the Vmedia protocol to deliver the compressed concentric mosaic bitstream just-in-time over the Internet. Only the bitstream segments corresponding to the current view are streamed over the Internet. The delivered bitstream segments are managed by a local Vmedia cache, so that frequently used bitstream segments do not need to be streamed over the Internet repeatedly, and a RBC bitstream larger than the memory capacity can be easily handled. Combining RBC and Vmedia, a concentric mosaic interactive browser is developed through which the user can freely wander in a realistic environment, e.g., rotate around, walk forward/backward, and sidestep, even under a tight bandwidth.

title: Context-based multiscale classification of document images using wavelet coefficient distributions
abstract: In this paper, an algorithm is developed for segmenting document images into four classes: background, photograph, text, and graph. Features used for classification are based on the distribution patterns of wavelet coefficients in high frequency bands. Two important attributes of the algorithm are its multiscale nature-it classifies an image at different resolutions adaptively, enabling accurate classification at class boundaries as well as fast classification overall-and its use of accumulated context information for improving classification accuracy.

title: Construct robust rule sets for classification
abstract: We study the problem of computing classification rule sets from relational databases so that accurate predictions can be made on test data with missing attribute values. Traditional classifiers perform badly when test data are not as complete as the training data because they tailor a training database too much. We introduce the concept of one rule set being more robust than another, that is, able to make more accurate predictions on test data with missing attribute values. We show that the optimal class association rule set is as robust as the complete class association rule set. We then introduce the k-optimal rule set, which provides predictions exactly the same as the optimal class association rule set on test data with up to k missing attribute values. This leads to a hierarchy of k-optimal rule sets in which decreasing size corresponds to decreasing robustness, and they all more robust than a traditional classification rule set. We introduce two methods to find k-optimal rule sets, i.e. an optimal association rule mining approach and a heuristic approximate approach. We show experimentally that a k-optimal rule set generated by the optimal association rule mining approach performs better than that by the heuristic approximate approach and both rule sets perform significantly better than a typical classification rule set (C4.5Rules) on incomplete test data.

title: Securing distributed adaptation
abstract: Open architecture networks provide applications with fine-grained control over network elements. With this control comes the risk of misuse and new challenges to security beyond those present in conventional networks. One particular security requirement is the ability of applications to protect the secrecy and integrity of transmitted data while still allowing trusted active elements within the network to operate on that data. This paper describes mechanisms for identifying trusted nodes within a network and securely deploying adaptation instructions to those nodes while protecting application data from unauthorized access and modification. Promising experimental results of our implementation within the conductor adaptation framework are also presented, suggesting that such features can be incorporated into real networks.

title: A framework of integrating gene relations from heterogeneous data sources: an experiment on Arabidopsis thaliana
abstract: One of the most important goals of biological investigation is to uncover gene functional relations. In this study we propose a framework for extraction and integration of gene functional relations from diverse biological data sources, including gene expression data, biological literature and genomic sequence information. We introduce a two-layered Bayesian network approach to integrate relations from multiple sources into a genome-wide functional network. An experimental study was conducted on a test-bed of Arabidopsis thaliana. Evaluation of the integrated network demonstrated that relation integration could improve the reliability of relations by combining evidence from different data sources. Domain expert judgments on the gene functional clusters in the network confirmed the validity of our approach for relation integration and network inference.

title: Text and picture segmentation by the distribution analysis of wavelet coefficients
abstract: This paper presents an algorithm to segment text and picture in an image using two features based on the statistical distribution of the wavelet coefficients in high frequency bands. The algorithm breaks the image into blocks and classifies every block as background, text or picture according to the two features. The block size is variable so that the segmentation can be accurate at the boundary of two types and avoids misclassifying due to over-localized region analysis.