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Wavelet-based image coder with entropy-constrained lattice vector quantizer (ECLVQ)

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 1998年第8期45卷 1015-1030页

作者： Kim, WH Hu, YH Nguyen, TQ Los Alamos Natl Lab Los Alamos NM 87545 USA Univ Wisconsin Dept Elect & Comp Engn Madison WI 53706 USA

This paper presents an algorithm that jointly optimizes a lattice vector quantizer (LVQ) and an entropy coder in a subband coding at all ranges of bit rate. Estimation formulas for both entropy and distortion of lattice quantized subband images are derived. From these estimates, we then develop dynamic algorithm optimizing the LVQ and entropy coder together for a given entropy rate. Compared to previously reported min-max approaches, or approaches using asymptotic distortion bounds, the approach reported here quickly designs a highly accurate optimal entropy-constrained LVQ, The corresponding wavelet-based image coder also has better coding performance compared to other subband coders that use entropy-constrained LVQ, especially at low bit rates.

关键词： entropy coding image coding lattice vector quantization wavelet

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Classification improvement of local feature vectors over the KNN algorithm

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MULTIMEDIA TOOLS AND APPLICATIONS 2013年第1期64卷 197-218页

作者： Mejdoub, Mahmoud Ben Amar, Chokri Univ Sfax Res Grp Intelligent Machines ENIS Sfax Tunisia

The KNN classification algorithm is particularly suited to be used when classifying images described by local features. In this paper, we propose a novel image classification approach, based on local descriptors and the KNN algorithm. The proposed scheme is based on a hierarchical categorization tree that uses both supervised and unsupervised classification techniques. The unsupervised one is based on a hierarchical lattice vector quantization algorithm, while the supervised one is based on both feature vectors labelling and supervised feature selection method. The proposed tree improves the effectiveness of local feature vector classification and outperforms the exact KNN algorithm in terms of categorization accuracy.

关键词： Feature vectors Categorization Indexing Wavelets lattice vector quantization Semantic

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Multiresolution video coding based on Kalman filtering motion estimation

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JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2002年第3期13卷 348-362页

作者： Kuo, CM Hsieh, CH Chao, CP I Shou Univ Dept Informat Engn Kaohsiung 840 Taiwan I Shou Univ Dept Elect Engn Kaohsiung 840 Taiwan

Motion estimation plays an important role for the coding of video signals in the framework of multiresolution. The conventional block-matching algorithms achieve only integer pixel accuracy, if interpolation is not performed. In this paper, a multiresolution video coding system based on Kalman filtering motion estimation is presented. The Kalman filter-based motion estimation employs the predicted motion and measured motion to obtain the best estimate of motion vector at the lowest resolution subimage. The motion vector is used as the initial estimate of other subimages and then refined with the conventional block-matching algorithm. The multiresolution motion-compensated difference of each subband is encoded by lattice vector quantization according to their statistical properties, respectively. Simulation results show that the proposed coding system not only improves the performance but also reduces the error propagation significantly when compared to the multiresolution video coding system based on conventional block-matching algorithms. (C) 2002 Elsevier Science (USA).

关键词： motion estimation multi resolution Kalman filter lattice vector quantization

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Split Table Extension: A Low Complexity LVQ Extension Scheme in Low Bitrate Audio Coding

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IEEE SIGNAL PROCESSING LETTERS 2010年第1期17卷 59-62页

作者： Wang, Jin Liu, Peilin Kong, Ji Ying, Rendong Shanghai Jiao Tong Univ Dept Elect Engn Shanghai 200240 Peoples R China

Embedded Algebraic vector quantization (EAVQ) is a fast and efficient lattice vector quantization (LVQ) scheme used in low-bitrate audio coding. However, a defect of EAVQ is the overload distortion which causes unpleasant noises in audio coding. To solve this problem, specific base codebook extension schemes should be carefully considered. In this letter, we present a novel EAVQ codebook extension scheme-Split Table Extension (STE), which splits a vector into two smaller vectors: one in the base codebook and the other in the split table. The base codebook and the split table are designed according to the appearance probability of quantized vectors in audio segments. Experiments on encoding multiple audio and speech sequences show that, compared with the existed Voronoi Extension scheme, STE greatly reduces computation complexity and storage requirement while achieving similar coding quality.

关键词： Embedded algebraic vector quantization lattice vector quantization split table extension Voronoi extension

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Scalable video transmission for the Internet

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COMPUTER NETWORKS AND ISDN SYSTEMS 1997年第15期29卷 1833-1842页

作者： Horn, U Girod, B Univ Erlangen Nurnberg Inst Telecommun D-91058 Erlangen Germany

Information systems and distributed applications for the Internet show a growing demand for real-time audiovisual services. Support for those services within the Internet protocol layer will be widely available in the near future. Nevertheless, the heterogeneous structure of the Internet remains to be a great obstacle for establishing rear-time video services. scalable video codecs, generating bit-streams decodable at different rates, have been proposed to address the heterogeneity problem. In this article we present a scalable codec based on a spatio-temporal resolution pyramid combined with lattice vector quantization for efficient compression. With this approach additional lower,bit-rate layers can be transmitted at the same overall bit-rate as needed by an H.263 single-layer transmission. The complexity of the codec is sufficiently low to allow software-only implementations of Internet video services. This is demonstrated on an Internet video server application. (C) 1997 Elsevier Science B.V.

关键词： Internet video scalable video coding software-only video codec spatio-temporal resolution pyramid lattice vector quantization

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A comparison of lossy compression methods on still and hyperspectral images

A comparison of lossy compression methods on still and hyper...

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Conference on Mathematics of Data/Image Coding, Compression and Encryption V, with Applications

作者： Serra-Sagristà, J Borrell, J Univ Autonoma Barcelona Dept Comp Sci Escola Tecn Super Engn E-08193 Cerdanyola Del Valles Barcelona Spain

ISBN: (纸本)0819445606

The statistical dependency across levels (or scales) in the wavelet transform of natural images can be effectively exploited to design state-of-the-art image coders. This paper presents a comparative evaluation of three such algorithms, although only one of these algorithms is detailed to a larger extend. The first method explicitly models the conditional statistics of the wavelet coefficients for bit-plane encoding. The second method performs an implicit sorting of the coefficients by sending zerotree symbols. Both methods produce an embedded code, i.e. the optimal image at any rate can be identified with the truncated bitstream of the highest possible rate, but the second algorithm does not depend as much as the first on the use of arithmetic coding. The third method is based on lattice vector quantization: statistical dependency within the wavelet transform is taken into account by conditioning the encoding of the vector norm on the value of a predictor. All three algorithms yield approximately comparable experimental results for different corpuses of images. This supports our view that the essence of high performance image compression is a careful modeling of the conditional image statistics.

关键词： image coding hyperspectral images lattice vector quantization wavelet transform

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High dimension lattice vector quantizer design for generalized Gaussian distributions

High dimension lattice vector quantizer design for generaliz...

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IEEE International Conference on Image Processing (ICIP 2007)

作者： Fonteles, Leonardo Hidd Antonini, Marc CNRS I3S Lab 2000 Route LuciolesBP 121 F-06903 Sophia Antipolis France

ISBN: (纸本)9781424414369

LVQ is a simple but powerful tool for vector quantization and can be viewed as a vector generalization of uniform scalar quantization. Like VQ, LVQ is able to take into account spatial dependencies between adjacent pixels as well as to take advantage of the n-dimensional space filling gain. However, the design of a lattice vector quantizer is not trivial particularly when one wants to use vectors with high dimensions. Indeed, using high dimensions involves lattice codebooks with a huge population that makes indexing difficult. On the other hand, in the framework of wavelet transform, a bit allocation across the subbands must be done in an optimal way. The use of VQ and the lack of non asymptotical distortion-rate models for this kind of quantizers make this operation difficult. In this work we focus on the problem of efficient indexing and optimal bit allocation and propose efficient solutions.

关键词： image compression lattice vector quantization generalized Gaussian distribution bit allocation

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Analysis of compression methods applied to hyperspectral images

Analysis of compression methods applied to hyperspectral ima...

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Conference on Image and Signal Processing for Remote Sensing VIII

作者： Serra-Sagristà, J Gavilán, D Minguillón, J Univ Autonoma Barcelona Dept Comp Sci Escola Tecn Super Engn E-08193 Cerdanyola Del Valles Barcelona Spain

ISBN: (纸本)081944667X

Several well-known methods for lossy compression of still images are here analyzed to evaluate their performance for hyperspectral images. The lossy compression methods discussed are the JPEG standard, and four approaches based on the Wavelet Transform: the Embedded coding of ZeroTree wavelet coefficients, the Set Partitioning in Hierarchical Trees, a lattice vector Quantizer, and the new JPEG2K. Experiments are first performed on corpuses of natural grayscale still images to provide a general framework of the performance of each method. Then experiments are performed on several hyperspectral images taken with CASI and AVMS sensors. Experiments show that it is possible to employ the basic lossy compression methods for hyperspectral image coding. The wavelet-based approaches produce results consistently better than the JPEG: JPEG can not achieve compression ratios above 75: 1;on the other side, with EZT, SPIHT and LVQ compression ratios of 250:1 or higher may be reached. For JPEG2K, higher compression ratios than JPEG may also be reached, but with a PSNR quality lower than the three other techniques. At compression ratios about 8: 1, the wavelet methods yield results 1.5 dB better than those of JPEG. These results help to explain why JPEG2K standard uses the WT instead of the DCT.

关键词： still image coding multi- and hyperspectral images wavelet/subband transform lattice vector quantization JPEG2K standard

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Techniques for Multistage A₂ lattice Structure Construction and Calculation using Triangular Number (T_n) and Square Number (S_n)

Techniques for Multistage <i>A</i><sub>2</sub> Lattice Struc...

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2nd International Conference on Computer Engineering and Applications

作者： Syafalni, Infall Salleh, M. F. M. Univ Sains Malaysia Sch Elect & Elect Engn George Town Malaysia

ISBN: (纸本)9780769539829

In this paper, the connection between triangular number (T-n), square number (S-n) for construction and counting the A(2) lattice number in lattice vector quantization is presented. We also observe that unique hexagonal A(2) lattice can be formed from two triangular numbers (T-n) and a square number (S-n) combination. Furthermore, the number of multistage A(2) lattice (W-n) is derived from A(2) lattice number (L-n). Analysis of the number of lattice points produced is also presented. Thus, this analysis produces a new way to calculate the bit rate.

关键词： component triangular number square number A(2) lattice number lattice vector quantization

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Adaptive lossy compression and classification of hyperspectral images

Adaptive lossy compression and classification of hyperspectr...

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Conference on Image and Signal Processing for Remote Sensing VI

作者： Minguillón, J Pujol, J Serra-Sagristà, J Ortuño, I Guitart, P Autonomous Univ Barcelona Combinator & Digital Commun Grp Dept Comp Sci E-08193 Barcelona Spain

ISBN: (纸本)081943826X

In this paper several methods for image lossy compression are compared in order to find adaptive schemes that may improve compression performance for hyperspectral images under a classification accuracy constraint. Our goal is to achieve high compression ratios without degrading classification accuracy too much for a given classifier. Lossy compression methods such as JPEG, three-dimensional JPEG, a tree structured vector quantizer, a zero-tree wavelet encoder, and a lattice vector quantizer have been used to compress the image before the classification stage. Classification is carried out through classification trees. Two kinds of classification trees are compared: one-stage trees, which classify the input image using only a single classification stage;and multi-stage trees, which use a mixed class that delays the classification of problematic pixels for which the accuracy achieved in the current stage is not enough. Our experiments indicate that is is possible to achieve high compression ratios while maintaining the classification accuracy. It is also shown that compression methods that take advantage of the high band correlation of hyperspectral images provide better results and become more flexible for a real case scenario. As compared to one-stage trees, the employment of multi-stage trees increases the classification accuracy and reduces the classification cost.

关键词： hyperspectral image compression and classification JPEG standard tree structured vector quantization lattice vector quantization zero-tree encoding classification trees

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