Hierarchical Vector Quantization Research Articles

A Weight Based Hierarchical Vector Quantization Algorithm for Space Environment Volume Data

Visualization has been widely applied in space environment domain. However, compressed volume rendering algorithms based on VQ are concerned on fidelity and compression rate, not combined with specific application. To fulfill the specific visualization requirements for space environment volume data, an application-driven compression and rendering algorithm is proposed, which is Weight Based Hierarchical Vector Quantization (WHVQ). The volume data is initially partitioned into disjoint 4³ blocks. Weights are assigned to the blocks according to their importance. The blocks are then decomposed into a three level hierarchical representation and each block is represented by a mean value and two detail vectors. To the top two levels, a splitting based on principal component analysis and weight is adopted to form their initial codebooks. Then, LBG algorithm based on weight is conducted for codebook refinement and quantization. The experimental results show that WHVQ is able to improve the quality of reconstruction in interested area on the premise of the good overall fidelity.

A Weight Based Hierarchical Vector Quantization Algorithm for Space Environment Volume Data

空间环境数据可视化是空间环境预报和服务的重要手段.目前基于矢量量化的压缩体绘制算法均只考虑了数据特点和压缩效果，并未结合具体应用需求.为适应空间环境体数据可视化具体应用需求，提出了一种应用驱动的压缩体绘制算法——基于权重的层次矢量量化算法（WHVQ）.算法将体数据划分为4³的数据块，并为数据块设置权重，重点关注区域的数据块赋予相对较大的权重值.然后，对各数据块用三层结构表示.最后，对次高层和最高层分别采用基于权重的主成分分析分裂法产生初始码书，并采用基于权重的LBG算法进行码书优化和量化.实验结果表明，该算法能够在保证整体压缩效果的同时，提升局部重点关注区域的重构质量.

An efficient and fast GPU-based algorithm for visualizing large volume of 4D data from virtual heart simulations

Abstract An efficient and fast visualization algorithm is important for analyzing a large volume of physical data conformed to 3D anatomical geometry that evolves with time (i.e., 4D data). Such 4D visualization helps to study the evolution of cardiac excitation waves in normal and pathological conditions and understand the mechanisms underlying the genesis and maintenance of cardiac arrhythmias. However, due to limited hardware resources, so far we have not found any report about real time methods to visualize a large volume of 4D data of virtual heart simulation data. In this study, we propose a GPU-based method to address this issue, our method consists of two phases, and the first is the data compression phase, implementing an improved hierarchical vector quantization method with N-nearest neighbor searching strategy in GPU, which reduces compression time dramatically. In the second phase, the compressed data is directly decompressed in GPU and rendered with ray casting method. What is more, an adaptive sampling strategy and empty space skipping methods are further used to accelerate the rendering process, resulting in a high rendering speed. The proposed method has been evaluated for the visualization of large time-varying cardiac electrophysiological simulation data by using our simulation datasets and has achieved promising results. For about 27G bytes dataset, our method can render the data with above 35 frames per second (FPS), which exceeds the real-time frame rate for interactive observing. It significantly decreases the time in the compression phase and achieves real time rendering speed with high image quality in the visualization phase, which demonstrates the accuracy and efficiency of our method.

A near lossless compression domain volume rendering algorithm for floating-point time-varying volume data

Compressing floating-point time-varying volume data and achieving both high compression rate and near lossless are challenging. This paper proposes a compression domain volume rendering (CDVR) approach based on hierarchical vector quantization (HVQ) and perfect spatial hashing (PSH) techniques. First, a HVQ process is applied to the first frame to obtain codebooks and index volumes. Then, a sparse residual volume (SRV) is computed by differencing the first frame and the recovery volume, which is reconstructed by utilizing the codebooks and the index volumes. Difference volumes are calculated by differencing the adjacent frame pairs of the time-series. Thereafter, both the SRV and the difference volumes are compressed by means of PSH technique. To render the time-series, the codebooks, the index volumes and the results of PSH are decompressed on-the-fly in constant time in GPU. In addition, a high compression rate is achieved by HVQ and PSH, and the compression is near lossless. The results on varied datasets verify that the proposed method can achieve the high compression rate and near lossless compression quality for floating-point time-varying volume data, as well as high efficient CDVR.

Fast and adaptive detection of pulmonary nodules in thoracic CT images using a hierarchical vector quantization scheme.

Computer-aided detection (CADe) of pulmonary nodules is critical to assisting radiologists in early identification of lung cancer from computed tomography (CT) scans. This paper proposes a novel CADe system based on a hierarchical vector quantization (VQ) scheme. Compared with the commonly-used simple thresholding approach, the high-level VQ yields a more accurate segmentation of the lungs from the chest volume. In identifying initial nodule candidates (INCs) within the lungs, the low-level VQ proves to be effective for INCs detection and segmentation, as well as computationally efficient compared to existing approaches. False-positive (FP) reduction is conducted via rule-based filtering operations in combination with a feature-based support vector machine classifier. The proposed system was validated on 205 patient cases from the publically available online Lung Image Database Consortium database, with each case having at least one juxta-pleural nodule annotation. Experimental results demonstrated that our CADe system obtained an overall sensitivity of 82.7% at a specificity of 4 FPs/scan. Especially for the performance on juxta-pleural nodules, we observed 89.2% sensitivity at 4.14 FPs/scan. With respect to comparable CADe systems, the proposed system shows outperformance and demonstrates its potential for fast and adaptive detection of pulmonary nodules via CT imaging.

An Efficient Compressed Volume Rendering Algorithm Based on GPU

An efficient compressed volume rendering algorithm is presented. Firstly, the original volume data is compressed by a content-based classified hierarchical vector quantization algorithm. Secondly, the compressed volume data is then transferred to Graphic Processing Unit and decompressed in real time, subsequently, the decompressed data is rendered by a three-dimensional textures mapping method to accelerate the speed of rendering. Experimental results show that, in addition to reasonable fidelity and faster rendering speed, the presented algorithm can obtain multiple levels of detail on the off-the-shelf graphic hardware.

An efficient compression scheme for 4-D medical images using hierarchical vector quantization and motion compensation

This paper proposes an efficient compression scheme for compressing time-varying medical volumetric data. The scheme uses 3-D motion estimation to create a homogenous preprocessed data to be compressed by a 3-D image compression algorithm using hierarchical vector quantization. A new block distortion measure, called variance of residual (VOR), and three 3-D fast block matching algorithms are used to improve the motion estimation process in term of speed and data fidelity. The 3-D image compression process involves the application of two different encoding techniques based on the homogeneity of input data. Our method can achieve a higher fidelity and faster decompression time compared to other lossy compression methods producing similar compression ratios. The combination of 3-D motion estimation using VOR and hierarchical vector quantization contributes to the good performance.

A Content-based Classified Hierarchical Vector Quantization Algorithm for Volume Compression

An improved volumetric compression algorithm is presented in this paper. Histogram technique is used for analyzing the trait of volume data. The volume data is then partitioned into volume bricks which will be classified into two groups, the blocks with meaningless information as one group(also called empty blocks), and those with meaningful information as the other group(also called object blocks). An efficient hierarchical VQ is applied to compress object blocks while for empty blocks, nothing is saved. Compare with analogous Volume Compression algorithm, experimental results demonstrate the proposed algorithm not only can improve the compression rate significantly on the premise of the good quality of reconstruction image, but also can obtain fast decoding speed.

Multiresolution image VQ compression by color codebook reordering

In this paper, a method to reach multiresolution VQ is proposed. A reordering process of color vectors from the codebook is performed. This yields to obtain an index image very close to the initial luminance image that can be obtained from any color image, but at a lower resolution. That way, the VQ technique can be applied once again on this new index image. In other words, a hierarchical and multiresolution VQ is defined. At each stage of the VQ compressed image, one is able to reconstruct it using the initial color codebook and the index one.A comparison of the proposed method and classical Hierarchical VQ (HVQ) scheme at different compression levels is performed to judge the performance and the robustness of the hybrid method. B oth objective and subjective measures are used.This work is a preliminary work to compress high dimension images at different levels.

Considerations of data handling system of a spaceborne imaging spectrometer with onboard data compression

This paper outlines the data handling system (DHS) of a spaceborne hyperspectral imager with an onboard data compressor. The data compression techniques to be used are successive approximation multistage vector quantization and hierarchical self-organizing cluster vector quantization. Considerations and implementation aspects of the DHS related to the onboard data compression are addressed. The impact of anomalies (spikes, saturation, etc.) in the raw data on compression performance is evaluated for the purpose of determining whether or not onboard data scrubbing is required before compression. The evaluation results show that anomalies in raw data have no significant effect on compression. This paper evaluates the impact of preprocessing and the conversion of raw data to radiance units on data compression using remote sensing applications to examine whether or not they should be applied on board before compression. The evaluation results show that preprocessing and radiometric conversion applied either before or after compression have no impact on an application using leaf area index but have impact on a target detection application using spectral unmixing. This paper also examines the combination of the two compression techniques to see if there is a performance improvement over a single technique. The experimental results show that the combined compression system does not perform better than either technique alone. Lastly, the resilience of the two compression techniques against bit errors caused by single event upsets is examined. The experimental results show that there is no loss of data fidelity when the error rate is ≤10–6.

Trading off quality and complexity for a HVQ-based video codec on portable devices

Bandwidth and processing requirements of multimedia applications typically exceed capabilities of portable terminals with current technology. Applications should hence be able to accommodate their requirements to run on these devices. In this paper, we provide a performance characterization of a video codec based on techniques such as hierarchical vector quantization which trade off complexity and reproduction quality. Comparison with standard codecs shows a remarkable reduction of coding times, such that real-time coding/decoding of video becomes possible even on low-power devices. This complexity reduction is counterbalanced by reproduction quality impairment. Nevertheless, for application such as video-conference, subjective quality seems to be fairly acceptable. Our analysis also quantifies some limitations of low-power devices with current technology.

LUT Filters for Quantized Processing of Signals

We introduce a method to perform filtering on approximations (quantized versions) of the input signal, which lends itself to a practical implementation solely based on look-up tables (LUTs). The LUT filter approximates the performance of some traditional nonlinear filters at a fraction of the cost. The filter is divided into an approximation stage that is constant for all filters and a filtering stage, which is one LUT that can change in order to implement different filters. We introduce an overlapped hierarchical vector quantization (OHVQ) scheme that is used as the approximation stage. The output is produced by mapping the OHVQ codes to filtered data. Hence, all processing is done via LUTs, even though the filter size needs to be small because of typical OHVQ contraints. Switching among filters demands changing pointers to only one small LUT. Preliminary analysis and image processing examples are shown, demonstrating the efficacy of the proposed method.

Digital implementation of hierarchical vector quantization

A formal methodology drives the design and realization of a digital very large-scale integration (VLSI) device supporting hierarchical vector quantization (HVQ) in computation-intensive coding applications. The hardware-oriented model-selection approach enhances the Minimum Description Length criterion with circuit-related aspects that allow consistent and efficient design. The resulting model parameters drive the subsequent realization in digital circuitry, which has first been implemented in field-programmable gate array (FPGA) technology to verify its correctness. The eventual VLSI realization results in an HVQ chip providing cost-effective, computationally efficient real-time performances. Real-world applications support the consistency of the vector quantization approach and the effectiveness of the HVQ device.

Variable complexity DCT approximations driven by an HVQ-based analyzer

Transform approximations are explored for speeding up the software compression of images and video. Approximations are used to replace the regular discrete cosine transform (DCT) whenever only a few DCT coefficients are actually encoded. We employ a novel hierarchical vector quantization (HVQ)-based image analyzer to drive a bank of coders, thus switching to the fastest coder depending on the image contents: smoother areas would produce less bits than detailed ones but faster. The HVQ analyzer is fast and simple enough to not offset the complexity gains brought by the DCT approximations. The approximations in this paper are applicable to high compression environments, where lower complexity is also required.

Very fast JPEG compression using hierarchical vector quantization

We derive a JPEG compliant image compressor, which is based on hierarchical vector quantization (HVQ). The goal is to reduce complexity while increasing compression speed. For each block, the DCT DC coefficient is encoded in the regular way, while the residual is mapped through HVQ to a precomputed bit stream corresponding to the compressed DCT AC coefficients. Approximation quality is generally good for high compression ratios. Color Fax is one possible application target for the proposed system.

Channel-matched hierarchical table-lookup vector quantization

In this correspondence, we propose a channel-matched hierarchical table-lookup vector quantizer for a discrete memoryless channel. We show that this scheme simultaneously provides low encoding complexity and robustness against transmission noise. Additionally, we propose the use of lookup tables for transcoding in heterogeneous networks. We show, through an example motivated by an application in multicasting, that the proposed table-lookup transcoding approach is an elegant alternative to the expensive and delay-introducing method of transcoding achieved by decoding and re-encoding.

Variable-rate hierarchical vector quantization with variance classification

Because the bit rate can be adjusted depending on the network traffic situation, the variable bit rate is more suitable than a fixed rate for transmitting images on the network. In this paper, a new variable rate hierarchical classified vector quantization (VHCVQ) scheme is proposed. This technique is suitable for progressive trans- mission. In our two-stage VHCVQ scheme, we use the differences between the first-stage input vectors and the first-stage quantized vectors, called residual vectors, to code the image in the second stage. Usually the values between these differences are small and each is close to one another. Using the difference to code the image may reduce the codebook size and the bit rate as well. In our simu- lation, using these differences reduced the bit rate by about 0.075 bpp. © 2000 John Wiley & Sons, Inc. Int J Imaging Syst Technol, 11, 138 -143, 2000

A texture thesaurus for browsing large aerial photographs

A texture-based image retrieval system for browsing large-scale aerial photographs is presented. The salient components of this system include texture feature extraction, image segmentation and grouping, learning similarity measure, and a texture thesaurus model for fast search and indexing. The texture features are computed by filtering the image with a bank of Gabor filters. This is followed by a texture gradient computation to segment each large airphoto into homogeneous regions. A hybrid neural network algorithm is used to learn the visual similarity by clustering patterns in the feature space. With learning similarity, the retrieval performance improves significantly. Finally, a texture image thesaurus is created by combining the learning similarity algorithm with a hierarchical vector quantization scheme. This thesaurus facilitates the indexing process while maintaining a good retrieval performance. Experimental results demonstrate the robustness of the overall system in searching over a large collection of airphotos and in selecting a diverse collection of geographic features such as housing developments, parking lots, highways, and airports.

Adaptive hierarchical vector quantization for image coding: new results

Adaptive hierarchical algorithms of vector quantization (VQ) for image coding are proposed. First the basic codebook is generated adaptively using adaptive VQ, then the quadruplets of codes/indices in the so-called zigzag order are coded into higher level (second and third levels) codes by creating the second- and third-level index codebooks to reduce the redundancy presented in the codes. Partially matched quadruplets are also encoded in the second and third layers using the index codebooks along with corresponding correction schemes. The third-layer encoding achieves a better compression ratio than a two-layer encoding scheme, which was shown to be optimal when partial encoding was not adopted. This three-layer coding scheme achieves better compression with no extra distortion and little extra computation. Experiments show encouraging results.

Multi-stage hierarchical address vector quantization for image coding

Abstract A new vector quantization (VQ) scheme for encoding images is presented. The proposed scheme is called multi-stage hierarchical address vector quantization and it combines the concepts of multi-stage VQ (MSVQ), hierarchical VQ (HVQ) and address VQ (AVQ). Its encoder consists of several stages where the HVQ or AVQ is applied while the larger image blocks are divided into smaller sub-blocks. Digital decimation and interpolation techniques are used to reduce the blocking efTect in the reconstructed images. Experimental results show thai the proposed coding scheme performs better than HVQ, MSVQ, AVQ, multi-layer address VQ and memoryless VQ systems.