Enabling telepresence using depth-based new view rendering requires the compression and transmission of dynamic depth maps and video from multiple cameras. The telepresence application places additional requirements on the compressed representation, such as preservation of depth discontinuities, low complexity decoding, and amenability to real-time rendering using graphics cards. We propose a simplified triangular mesh representation that can be encoded efficiently. The mesh geometry is encoded using a binary triangle tree structure and the depths at the tree nodes are differentially coded. By matching the tree traversal to the mesh rendering order, both depth map decoding and triangle strip generation for efficient rendering are achieved simultaneously. The proposed scheme naturally lends itself to coding segmented foreground layers. Our experiments show a significant improvement in rendering speeds at similar compression rates using the new mesh based depth map representation when compared to independent representations for compression and rendering.

We consider applications using depth-based image-based rendering (IBR), where the synthesis of arbitrary views occur at a remote location, necessitating the compression and transmission of depth maps. Traditional image compression has been designed to provide maximum perceived visual quality, and a direct application is sub-optimal for depth-map compression, since depth-maps are not directly viewed. In other words, the sensitivity of the rendering error depends on the image content as well as on the depth map, we propose two improvements to take this into account. Firstly, we consider region-of-interest (ROI) coding, where we identify those regions of the image where accurate depth is most crucial. Secondly, we reshape the dynamic range of the depth map. Our experiments show a significant improvement in coding gain (1.1 dB) and rendering quality when we integrated these two improvements into a standard JPEG-2000 coder.

Arithmetic coding is commonly used for entropy coding in image compression. It has been shown that arithmetic coding has a higher coding efficiency than other entropy coding schemes. However, it has a severe drawback when used in an error prone environment. Since arithmetic coding makes it impossible to precisely locate an error, a large portion of a bitstream must be discarded when an error occurs. In traditional packetization techniques, the bitstream is segmented to packets and the arithmetic encoder is reset at the beginning of each packet. However, the re-initialization will significantly reduce the coding efficiency of the arithmetic coder. We propose a novel technique to improve the error resilience of arithmetic coding. This new technique adds segment markers in the coded bitstream without the re-initialization of the arithmetic coder, which helps retain the correctly decoded segments within the bitstream improving the quality of the reconstructed image. Segment markers have been adopted in both MPEG-4 and JPEG 2000. Experimental results show that THE segment marker significantly improves the quality of reconstructed image with minor increase in bitstream size.

This paper describes the visual texture compression scheme adopted for MPEG-4 international standard. The scheme is based on the concept of multiscale zerotree wavelet entropy coding (MZTE) technique that provides different levels of scalability layer in terms of either spatial resolutions or picture quality. MZTE provides much improved compression efficiency and fine-gradual scalabilities. The MZTE scheme is adopted as the baseline technique for the visual texture coding profile in both MPEG4 video group and SNHC group.

A novel hybrid wavelet video coding algorithm termed video significance-linked connected component analysis (VSLCCA) is developed for very low bit-rate applications. In the proposed VSLCCA codec, first, fine-tuned motion estimation based on the H.263 Recommendation is developed to reduce temporal redundancy, and exhaustive overlapped block motion compensation is utilized to ensure coherency in motion compensated error frames. Second, the wavelet transform is applied to each coherent motion compensated error frame to attain global energy compaction. Third, significant fields of wavelet-transformed error frames are organized and represented as significance-linked connected components so that both the within-subband clustering and the cross-scale dependency are exploited. Last, the horizontal and vertical components of motion vectors are encoded separately using adaptive arithmetic coding while significant wavelet coefficients are encoded in bit-plane order by using high order Markov source modeling and adaptive arithmetic coding. Experimental results on eight standard MPEG-4 test sequences show that for intraframe coding, on average the proposed codec exceeds H.263 and ZTE (zero-tree entropy) in peak signal-to-noise ratio by as much as 2.07 and 1.38 dB at 28 kbit/s, respectively. For entire sequence coding, VSLCCA is superior to H.263 and ZTE by 0.35 and 0.71 dB on average, respectively.

Recent success in wavelet image coding is mainly attributed to a recognition of the importance of data organization and representation. There have been several very competitive wavelet coders developed, namely, Shapiro's (1993) embedded zerotree wavelets (EZW), Servetto et al.'s (1995) morphological representation of wavelet data (MRWD), and Said and Pearlman's (see IEEE Trans. Circuits Syst. Video Technol., vol.6, p.245-50, 1996) set partitioning in hierarchical trees (SPIHT). We develop a novel wavelet image coder called significance-linked connected component analysis (SLCCA) of wavelet coefficients that extends MRWD by exploiting both within-subband clustering of significant coefficients and cross-subband dependency in significant fields. Extensive computer experiments on both natural and texture images show convincingly that the proposed SLCCA outperforms EZW, MRWD, and SPIHT. For example, for the Barbara image, at 0.25 b/pixel, SLCCA outperforms EZW, MRWD, and SPIHT by 1.41 dB, 0.32 dB, and 0.60 dB in PSNR, respectively. It is also observed that SLCCA works extremely well for images with a large portion of texture. For eight typical 256x256 grayscale texture images compressed at 0.40 b/pixel, SLCCA outperforms SPIHT by 0.16 dB-0.63 dB in PSNR. This performance is achieved without using any optimal bit allocation procedure. Thus both the encoding and decoding procedures are fast.

Perhaps, Sarnoff Corporation's zerotree entropy (ZTE) coder is the most successful wavelet video coder published so far which exploits the statistical properties of wavelet-transformed images by utilizing novel data representation and organization strategies. A high performance hybrid video coding algorithm termed video significance-linked connected component analysis (VSLCCA) is developed. It is quite encouraging that, at least empirically convinced, the wavelet transform with aids of those published innovative data representation and organization methods can be an invaluable asset in video coding if motion-compensated error frames are coherent. In VSLCCA, time domain motion estimation followed by exhaustive overlapped block motion compensation is utilized to ensure coherency, and then wavelet transform is applied to each error frame with significant wavelet coefficients being encoded by highly efficient SLCCA technique. Experimental results on standard MPEG-4 test sequences show that VSLCCA is superior to H.263 and ZTE by 0.48 dB and 0.77 dB on average, respectively.

In the paper, our high performance significance-linked connected component analysis (SLCCA) image compression algorithm is extended to three-dimensional wavelet coding resulting in a highly efficient and scalable video codec termed 3DSLCCA. Advantageous features of 3DSLCCA include low computational complexity, rate, spatial, temporal, and SNR scalability, and prevention of error propagation at the price of acceptable degradation in both objective and subjective quality when compared to the state-of-the-art H.263 standard.

Recent success in wavelet image coding is mainly attributed to recognition of the importance of data organization and representation. There have been several very competitive wavelet coders developed, namely, Shapiro's embedded zerotree wavelets (EZW), Servetto et al.'s morphological representation of wavelet data (MRWD), and Said and Pearlman's set partitioning in hierarchical trees (SPIHT). In this paper, we develop a novel wavelet image coder called significance-linked connected component analysis (SLCCA) of wavelet coefficients that extends MRWD by exploiting both within-subband clustering of significant coefficients and cross-subband dependency in significant fields. Extensive computer experiments on both natural and texture images show convincingly that the proposed SLCCA outperforms EZW, MRWD, and SPIHT. For example, for the "Barbara" image, at 0.5 bpp SLCCA outperforms EZW and SPIHT by 1.75 dB and 0.89 dB in PSNR, respectively. This outstanding performance is achieved without using any optimal bit allocation procedure, thus both the encoding and decoding procedures are fast.

Recent success in wavelet image coding is mainly attributed to the recognition of importance of data organization and representation, There have been several very competitive wavelet coders developed, namely, embedded zerotree wavelets (EZW), morphological representation of wavelet data (MRWD), and set partitioning in hierarchical trees (SPIHT). In this paper, we develop a novel wavelet image coder called significance-linked connected component analysis (SLCCA) of wavelet coefficients that extends MRWD by exploiting both within-subband clustering of significant coefficients and cross-subband dependency in significant fields. Computer experiments on both natural and texture images show convincingly that the proposed SLCCA outperforms EZW, MRWD, and SPIHT as well. For example, for the "Barbara" image, at 0.50 bpp SLCCA outperforms EZW and SPIHT by 1.71 dB and 0.85 dB in PSNR, respectively. It is also observed that SLCCA works extremely well for images with a large portion of texture. This outstanding performance is achieved without using any optimal bit allocation procedure. Thus both the encoding and decoding procedures are fast.

A linear decision binary tree structure is proposed in constructing piecewise linear classifiers with the genetic algorithm (GA) being shaped and employed at each nonterminal node to search for a linear decision function optimal in the sense of maximum impurity reduction. The methodology works for both the two-class and multiclass cases. In comparison to several other well known methods, the proposed binary tree-genetic algorithm (BTGA) is demonstrated to produce a much lower cross validation misclassification rate. Finally, a modified BTGA is applied to the important pap smear cell classification. This results in a spectrum for the combination of the highest desirable sensitivity along with the lowest possible false alarm rate. The multiple choices offered by the spectrum for the sensitivity-false alarm rate combination will provide the flexibility needed for the pap smear slide classification.