Ulug Bayazit

Variable-length constrained-storage tree-structured vector quantization

Constrained storage vector quantization, (CSVQ), introduced by Chan and Gersho (1990, 1991) allows for the stagewise design of balanced tree-structured residual vector quantization codebooks with low encoding and storage complexities. On the other hand, it has been established by Makhoul et al. (1985), Riskin et al. (1991), and by Mahesh et al. (see IEEE Trans. Inform. Theory, vol.41, p.917-30, 1995) that variable-length tree-structured vector quantizer (VLTSVQ) yields better coding performance than a balanced tree-structured vector quantizer and may even outperform a full-search vector quantizer due to the nonuniform distribution of rate among the subsets of its input space. The variable-length constrained storage tree-structured vector quantization (VLCS-TSVQ) algorithm presented in this paper utilizes the codebook sharing by multiple vector sources concept as in CSVQ to greedily grow an unbalanced tree structured residual vector quantizer with constrained storage. It is demonstrated by simulations on test sets from various synthetic one dimensional (1-D) sources and real-world images that the performance of VLCS-TSVQ, whose codebook storage complexity varies linearly with rate, can come very close to the performance of greedy growth VLTSVQ of Riskin et al. and Mahesh et al. The dramatically reduced size of the overall codebook allows the transmission of the code vector probabilities as side information for source adaptive entropy coding.

3-D Mesh Geometry Compression With Set Partitioning in the Spectral Domain

This paper explains the development of a highly efficient progressive 3-D mesh geometry coder based on the region adaptive transform in the spectral mesh compression method. A hierarchical set partitioning technique, originally proposed for the efficient compression of wavelet transform coefficients in high-performance wavelet-based image coding methods, is proposed for the efficient compression of the coefficients of this transform. Experiments confirm that the proposed coder employing such a region adaptive transform has a high compression performance rarely achieved by other state of the art 3-D mesh geometry compression algorithms. A new, high-performance fixed spectral basis method is also proposed for reducing the computational complexity of the transform. Many-to-one mappings are employed to relate the coded irregular mesh region to a regular mesh whose basis is used. To prevent loss of compression performance due to the low-pass nature of such mappings, transitions are made from transform-based coding to spatial coding on a per region basis at high coding rates. Experimental results show the performance advantage of the newly proposed fixed spectral basis method over the original fixed spectral basis method in the literature that employs one-to-one mappings.

Algorithmic modifications to SPIHT

This paper proposes several low complexity algorithmic modifications to the SPIHT (set partitioning in hierarchical trees) image coding method of Said and Pearlman (1996). The modifications exploit universal traits common to the real world images. Approximately 1-2% compression gain (bit rate reduction for a given mean squared error) has been obtained for the images in our test suite by incorporating all of the proposed modifications into SPIHT.

Adaptive Spectral Transform for Wavelet-Based Color Image Compression

Since different regions of a color image generally exhibit different spectral characteristics, the energy compaction of applying a single spectral transform to all regions is largely inefficient from a compression perspective. Thus, it is proposed that different subsets of wavelet coefficients of a color image be subjected to different spectral transforms before the resultant coefficients are coded by an efficient wavelet coefficient coding scheme such as that used in JPEG2000 or color set partitioning in hierarchical trees (CSPIHT). A quadtree represents the spatial partitioning of the set of high frequency coefficients of the color planes into spatially oriented subsets which may be further partitioned into smaller directionally oriented subsets. The partitioning decisions and decisions to employ fixed or signal-dependent bases for each subset are rate-distortion (R-D) optimized by employing a known analytical R-D model for these coefficient coding schemes. A compression system of asymmetric complexity, that integrates the proposed adaptive spectral transform with the CSPIHT coefficient coding scheme yields average coding gains of 0.3 dB and 0.9 dB in the Y component at 1.0 b/p and 2.5 b/p, respectively, and 0.9 dB and 1.35 dB in the U and V components at 1.0 b/p and 2.5 b/p, respectively, over a reference compression system that integrates the single spectral transform derived from the entire image with the CSPIHT coefficient coding scheme.

Region adaptive spectral transformation For wavelet based color image compression

In general, each region of a color image exhibits different spectral characteristics. Therefore, the energy compaction characteristic of a single global spectral transformation is rather weak for compression purposes. In this paper, we propose that different groups of wavelet coefficients of a color image be subjected to different spectral transformations prior to the spectral planes being coded by a wavelet-based image coder such as CSPIHT (Color Set Partitioning in Hierarchical Trees, [7]). The decomposition of the color image into such groups is succintly represented with a quadtree structure which is optimized for rate-distortion performance by means of a known analytical rate-distortion model for wavelet-based image codecs. The experiments show that, when integrated with the CSPIHT coder, the proposed region adaptive transformation method yields compression gains of 0.4–0.5 dB, on the average, for rates between 0.5bpp and 2.5bpp over the single global transformation method.

Significance map pruning and other enhancements to SPIHT image coding algorithm

This paper proposes several enhancements to the Set Partitioning in Hierarchical Trees (SPIHT) image coding algorithm without changing the original algorithms general skeleton. First and foremost, a method for significance map pruning based on a rate-distortion criterion is introduced. Specifically, the (Type A) sets of wavelet coefficients with small ratios of estimated distortion reduction to estimated rate contribution are deemed insignificant and effectively pruned. Even though determining such sets requires the computational complexity of the encoder to increase considerably with respect to the original SPIHT encoder, the original SPIHT decoder may still be used to decode the generated bitstream with a low computational complexity. The paper also proposes three low complexity enhancements by more sophisticated use of the adaptive arithmetic coder. Simulation results demonstrate that all these enhancements yield modest compression gains at moderate to high rates.

Predictive vector quantization of 3-D polygonal mesh geometry by representation of vertices in local coordinate systems

A large family of lossy 3-D mesh geometry compression schemes operate by predicting the position of each vertex from the coded neighboring vertices and encoding the prediction error vectors. In this work, we first employ entropy constrained extensions of the predictive vector quantization and asymptotically closed loop predictive vector quantization techniques that have been suggested in [3] for coding these prediction error vectors. Then we propose the representation of the prediction error vectors in a local coordinate system with an axis coinciding with the surface normal vector in order to cluster the prediction error vectors around a 2-D subspace. We adopt a least squares approach to estimate the surface normal vector from the non-coplanar, previously coded neighboring vertices. Our simulation results demonstrate that the prediction error vectors can be more efficiently vector quantized by representation in local coordinate systems than in global coordinate systems.

Macroblock data classification and nonlinear bit count estimation for low delay H.263 rate control

A new rate control method for low delay transmission of H.263 coded video is presented. The method is based on nonlinear MMSE (minimum mean squared error) estimation of the number of coding bits of a macroblock. A class is determined for each macroblock from its coding mode and quantized simple statistics of its data. Each combination of macroblock class and quantization parameter is mapped to a nonlinear estimate which is designed either online or online from empirical data. The quantization parameters of macroblocks of a frame are determined to attain near uniform spatial reconstruction quality as the total estimate of the number of coding bits closely approximates the targeted number of bits for the frame satisfying the delay and encoder buffer size constraints. Simulations demonstrate that the accuracy of the new macroblock layer rate control method is significantly better than the method of TMN5 and very competitive with the method of TMN8.

Postprocessing of decoded color images by adaptive linear filtering

This paper presents an image adaptive linear filtering method for the reconstruction of the RGB (red, blue, green) color coordinates of a pixel from the lossy compressed luminance/chrominance color coordinates. In the absence of quantization noise, the RGB coordinates of a pixel can be perfectly reconstructed by employing a standard, fixed filter whose support includes only the luminance/chrominance coordinates at the spatial location of the pixel. However, in the presence of quantization noise, a filter with a larger support, that also spatially extends over the luminance/ chrominance coordinate planes, is capable of exploiting the statistical dependence among the luminance/chrominance coordinate planes, and thereby yields more accurate reconstruction than the standard, fixed filter. We propose the optimal (in the minimum mean squared error sense) determination of the coefficients of this adaptive linear filter at the image encoder by solving a system of regression equations. When transmitted as side information to the image decoder, the filter coefficients need not incur significant overhead if they are quantized and compressed intelligently. Our simulation results demonstrate that the distortion of the decompressed color coordinate planes can be reduced by several tenths of a dB with negligible overhead rate by the application of our image adaptive linear filtering method. r 2002 Elsevier Science B.V. All rights reserved.

New frame rate up-conversion based on foreground/background segmentation

Frame rate up-conversion (FRUC) increases the quality of a video by increasing its temporal frequency. Motion compensated and non-motion compensated frame rate up conversion techniques make up the two main classes of techniques used in this area. Halo artifacts and jaggy edges cause the quality of video to be reduced both subjectively and objectively in these techniques. In this paper, we introduce a new method of motion compensated FRUC that uses foreground background segmentation to address these problems. It checks up the neighbors of gap pixels and deciding which neighbors should be used to interpolate or extrapolate according to their foreground or background identity. The current work reduces block artifacts by scaling motion vectors of existent frames to motion vectors of interpolated frames by using the foreground/background segmentation information. The proposed method improves the quality of video and obtains better PSNR results than the proposed methods of [9], [13] and [14].