Compression of the layered depth image
A layered depth image (LDI) is a new popular representation and rendering method for objects with complex geometries. Similar to a two-dimensional (2-D) image, the LDI consists of an array of pixels. However, unlike the 2-D image, an LDI pixel has depth information, and there are multiple layers at a pixel location. We develop a novel LDI compression algorithm that handles the multiple layers and the depth coding. The algorithm records the number of LDI layers at each pixel location, and compresses LDI color and depth components separately. For LDI layer with sparse pixels, the data is aggregated and then encoded. An empirical rate-distortion model is used to optimally allocate bits among different components. Compared with the benchmark compression tools such as JPEG-2000 and MPEG-4, our scheme improves the compression performance significantly.

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: Image compression with a hybrid wavelet-fractal coder
abstract: 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: 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: 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: 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: Joint image compression and classification with vector quantization and a two dimensional hidden Markov model
abstract: We present an algorithm to achieve good compression and classification for images using vector quantization and a two dimensional hidden Markov model. The feature vectors of image blocks are assumed to be generated by a two dimensional hidden Markov model. We first estimate the parameters of the model, then design a vector quantizer to minimize a weighted sum of compression distortion and classification risk, the latter being defined as the negative of the maximum log likelihood of states and feature vectors. The algorithm is tested on both synthetic data and real image data. The extension to joint progressive compression and classification is discussed.

title: Direct interesting rule generation
abstract: An association rule generation algorithm usually generates too many rules including a lot of uninteresting ones. Many interestingness criteria are proposed to prune those uninteresting rules. However, they work in post-pruning process and hence do not improve the rule generation efficiency. We discuss properties of informative rule set and conclude that the informative rule set includes all interesting rules measured by many commonly used interestingness criteria, and that rules excluded by the informative rule set are forwardly prunable, i.e. they can be removed in the rule generation process instead of post pruning. Based on these properties, we propose a direct interesting rule generation algorithm, DIG, to directly generate interesting rules defined by any of 12 interestingness criteria. We further show experimentally that DIG is faster and uses less memory than Apriori.

title: Independent triangles covering given vertices of a graph
abstract: Let G be a simple graph of order n, k a positive integer with n≥3k and X a set of any k vertices of G. We show that if the minimum degree G)≥(n + k)=2, then G contains k independent triangles covering all vertices of X ; and if the minimum degree (G)≥(n+2k)=2, then G contains k independent triangles such that each triangle contains exactly one vertex of X . The bounds on the minimum degree of G in above results are sharp. Some conjectures about independent triangles covering some given vertices are proposed.

title: Toward bridging the annotation-retrieval gap in image search by a generative modeling approach
abstract: While automatic image annotation remains an actively pursued research topic, enhancement of image search through its use has not been extensively explored. We propose an annotation-driven image retrieval approach and argue that under a number of different scenarios, this is very effective for semantically meaningful image search. In particular, our system is demonstrated to effectively handle cases of partially tagged and completely untagged image databases, multiple keyword queries, and example based queries with or without tags, all in near-realtime. Because our approach utilizes extra knowledge from a training dataset, it outperforms state-of-the-art visual similarity based retrieval techniques. For this purpose, a novel structure-composition model constructed from Beta distributions is developed to capture the spatial relationship among segmented regions of images. This model combined with the Gaussian mixture model produces scalable categorization of generic images. The categorization results are found to surpass previously reported results in speed and accuracy. Our novel annotation framework utilizes the categorization results to select tags based on term frequency, term saliency, and a WordNet-based measure of congruity, to boost salient tags while penalizing potentially unrelated ones. A bag of words distance measure based on WordNet is used to compute semantic similarity. The effectiveness of our approach is shown through extensive experiments.

title: Unsupervised Multiresolution Segmentation for Images with Low Depth of Field
abstract: Unsupervised segmentation of images with low depth of field (DOF) is highly useful in various applications. This paper describes a novel multiresolution image segmentation algorithm for low DOF images. The algorithm is designed to separate a sharply focused object-of-interest from other foreground or background objects. The algorithm is fully automatic in that all parameters are image independent. A multi-scale approach based on high frequency wavelet coefficients and their statistics is used to perform context-dependent classification of individual blocks of the image. Unlike other edge-based approaches, our algorithm does not rely on the process of connecting object boundaries. The algorithm has achieved high accuracy when tested on more than 100 low DOF images, many with inhomogeneous foreground or background distractions. Compared with he state of the art algorithms, this new algorithm provides better accuracy at higher speed.