Impact of vertex clustering on registration-based 3D dynamic mesh coding

Rate-distortion-optimized predictive compression of dynamic 3D mesh sequences

Lossless coding of image data has been a very active area of research in the field of medical imaging, remote sensing and document processing/delivery. While several lossless image coders such as JPEG and JBIG have been in existence for a while, their compression performance for encoding continuous-tone images were rather poor. Recently, several state of the art techniques like CALIC and LOCO were introduced with significant improvement in compression performance over traditional coders. However, these coders are very difficult to implement using dedicated hardware or in software using media processors due to their inherently serial nature of their encoding process. In this work, we propose a lossless image coding technique with a compression performance that is very close to the performance of CALIC and LOCO while being very efficient to implement both in hardware and software. Comparisons for encoding the JPEG- 2000 image set show that the compression performance of the proposed coder is within 2 - 5% of the more complex coders while being computationally very efficient. In addition, the encoder is shown to be parallelizabl at a hierarchy of levels. The execution time of the proposed encoder is smaller than what is required by LOCO while the decoder is 2 - 3 times faster that the execution time required by LOCO decoder.© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

3D triangle meshes are a common form for representing the geometry of static and dynamic 3D objects. They are employed already in many areas, e.g. e-commerce, video games, online museums, CGI or 3D animated films, etc. Static triangle meshes represent only a piecewise linear approximation of complex 3D objects. As a consequence the approximation error can be unacceptably high unless the number of triangles is sufficiently large. On the other hand a large number of triangles makes these meshes cumbersome to handle and expensive to store or to transmit. Consequently, there exists a demand for techniques for efficient compression of static and dynamic 3D meshes. In this article we start with basics on 3D meshes. Thereafter, we explain the key ideas behind different mesh compression approaches for static and dynamic 3D meshes, and highlight their similarities and differences. Finally, we introduce the upcoming MPEG standard for compression of dynamic 3D meshes, which is referred to as FAMC (Frame-based Animated Mesh Compression), and show comparative compression results.

We develop novel methods for compressing volumetric imagery that has been generated by single-platform (mobile) range sensors. We exploit the correlation structure inherent in multiple views in order to improve compression efficiency. We show that, for lossless compression, three-dimensional volumes compress more efficiently than two-dimensional (2D) images by a factor of 60%. Furthermore, our error metric for lossy compression suggests that accumulating more than nine range images in one volume before compression yields as much as a 99% improvement in compression performance over 2D compression.

Adaptive representation of dynamic 3D meshes for low-latency applications

The research and development of tools for genomic data compression has focused so far on data generated by second-generation sequencing technologies, while third-generation technologies, such as nanopore technologies, have received little attention in the data compression research community. In this paper, we investigate compression schemes for nanopore FASTQ files. We propose a nanopore quality scores compressor, called DualCtx, which yields significant improvements in compression performance with respect to the state-of-the-art. We also extend DualCtx to a full FASTQ compressor, termed DualFqz, by substituting DualCtx for the quality score compression module in a variant of Fqzcomp. We tested DualFqz and various existing compressors on a large nanopore data set. The results show that DualFqz achieves the best compression performance. The experiments also show that most current implementations of compressors fail to execute correctly on files with long variable length reads.

Towards coding for VoD application: An enhanced video compression system with a content-fitted recursive restoration network

Unstructured dynamic mesh for large movement and deformation

The growth of computational power of contemporary hardware causes technologies working with 3D-data to expand. Examples of the use of this kind of data can be found in geography or gaming industry. 3D-data may not be only static, but also dynamic.One way of animated 3D-data representation is expressing them by ”dynamic triangle mesh”. This kind of data representation is usually voluminous and needs to be compressed for efficient storage and transmission. In this paper, we are dealing with the influence of vertex clustering on dynamic mesh compression. The mesh is divided into vertex clusters based on the vertex movement similarity and compressed per-partes to achieve higher compression performance. We use Coddyac as a basic compression algorithm and extend it by adding well known clustering algorithms to demonstrate the efficiency of this approach. We also addres the choice of optimal clustering strategy for the Coddyac algorithm.Keywords3D dynamic meshesData compressionComputer animationCoddyacClustering

Learned video compression has drawn great attention and shown promising compression performance recently. In this article, we focus on the two components in the learned video compression framework, the conditional entropy model and quality enhancement module, to improve compression performance. Specifically, we propose an adaptive spatial-temporal entropy model for image, motion, and residual compression, which introduces a temporal prior to reduce temporal redundancy of latents and an additional modulated mask to evaluate the similarity and perform refinement. In addition, a quality enhancement module is proposed for predicted frame and reconstructed frame to improve frame quality and reduce the bitrate cost of residual coding. The module reuses decoded optical flow as a motion prior and utilizes deformable convolution to mine high-quality information from the reference frame in a bit-free manner. The two proposed coding tools are integrated into a pixel-domain residual coding–based compression framework to evaluate their effectiveness. Experimental results demonstrate that our framework achieves competitive compression performance in the low-delay scenario compared with recent learning-based methods and traditional H.265/HEVC in terms of Peak Signal-to-Noise Ratio (PSNR) and Multi-Scale Structural Similarity Index (MS-SSIM). The code is available at OpenLVC.

We introduce an efficient algorithm for real-time compression of temporally consistent dynamic 3D meshes. The algorithm uses mesh connectivity to determine the order of compression of vertex locations within a frame. Compression is performed in a frame to frame fashion using only the last decoded frame and the partly decoded current frame for prediction. Following the predictive coding paradigm, local temporal and local spatial dependencies between vertex locations are exploited. In this framework we present a novel angle preserving predictor and evaluate its performance against other state of the art predictors. It is shown that the proposed algorithm improves up to 25% upon the current state of the art for compression of temporally consistent dynamic 3D meshes.

In this paper we present StarNT, a dictionary-based fast lossless text transform algorithm. With a static generic dictionary, StarNT achieves a superior compression ratio than almost all the other recent efforts based on BWT and PPM. This algorithm utilizes ternary search tree to expedite transform encoding. Experimental results show that the average compression time has improved by orders of magnitude compared with our previous algorithm LIPT and the additional time overhead it introduced to the backend compressor is unnoticeable. Based on StarNT, we propose StarZip, a domain-specific lossless text compression utility. Using domain-specific static dictionaries embedded in the system, StarZip achieves an average improvement in compression performance (in terms of BPC) of 13% over bzip2-9, 19% over gzip-9, and 10% over PPMD.

[Summary form only given] Typically, the lossless compression of color images is achieved by separately compressing the three RGB monochromatic image components. The proposed method takes into account the fact that high spatial correlations exist not only within each monochromatic frame but also between similar spatial locations in adjacent monochromatic frames. Based on the observation that the prediction errors produced by the JPEG predictor in each RGB monochromatic frame present very similar structures, we propose two new chromatic predictors, called chromatic differential predictor (CDP) and classified CDP (CCDP), to capture the spectral dependencies between the monochromatic frames. In addition to prediction schemes, we consider context modeling schemes that take into account the prediction errors in spatially and/or spectrally adjacent pixels in order to efficiently encode the prediction errors. In order to demonstrate the advantage of the proposed lossless color image compression scheme, 5 different types of images are selected from the KODAK image set. All images are RGB 24 bpp color images with resolution 768/spl times/512. The experimental results demonstrate significant improvement in compression performance. Its fast implementation and high compression ratio may be a promising approach for the application of real-time color video compression.

Summary form only given. StarZip, a multi-copora text compression system, was introduced together with its transform engine StarNT. One of the key features of the StarZip compression system is to develop domain specific dictionaries and provide tools to develop such dictionaries. StarNT was utilized because it achieves a superior compression ratio than almost all the other recent efforts based on BWT and PPM. StarNT is a dictionary-based fast lossless text transform. The main idea is to record each English word with a representation of no more than three symbols. This transform maintains most of the original context information at the word level and provides an artificial strong context. It ultimately reduces the size of the transformed text that, in turn, is provided to a backend compressor. This data structure provides a very fast transform encoding with a low storage overhead. StarNT also treats the transformed codewords as an offset of words in the transform dictionary. The time complexity for searching a word in the dictionary is achieved in the transform decoder. Experimental results have shown that the average compression time has improved by orders magnitude compared to previous dictionary-based transform LIPT. The complexity and compression performance of bzip2, in conjunction with this transform, is better than both gzip and PPMD. Results from five copora have shown that StarZip achieved an average improvement in compression performance (in terms of BPC) of 13% over bzip2-9, 19% over gzip-9, and 10% over PPMD.

A recently introduced iterative complexity- and entropy-constrained subband quantization design algorithm is generalized and applied to medical image compression. In particular, the corresponding subband coder is used to encode computed tomography (CT) axial slice head images, where statistical dependencies between neighboring image subbands are exploited. Inter-slice conditioning is also employed for further improvements in compression performance. The subband coder features many advantages such as relatively low complexity and operation over a very wide range of bit rates. Experimental results demonstrate that the performance of the new subband coder is relatively good, both objectively and subjectively.