Zerotree Coding Research Articles

Visual Tracking Based on Discriminative Compressed Features

Visual tracking is a challenging research topic in the field of computer vision with many potential applications. A large number of tracking methods have been proposed and achieved designed tracking performance. However, the current state-of-the-art tracking methods still can not meet the requirements of real-world applications. One of the main challenges is to design a good appearance model to describe the target’s appearance. In this paper, we propose a novel visual tracking method, which uses compressed features to model target’s appearances and then uses SVM to distinguish the target from its background. The compressed features were obtained by the zero-tree coding on multiscale wavelet coefficients extracted from an image, which have both the low dimensionality and discriminate ability and therefore ensure to achieve better tracking results. The experimental comparisons with several state-of-the-art methods demonstrate the superiority of the proposed method.

SAR complex image data compression based on quadtree and zerotree Coding in Discrete Wavelet Transform Domain: A Comparative Study

Abstract A SAR complex image data compression algorithm based on quadtree coding (QC) in discrete wavelet transform (DWT) domain (QC-DWT) is proposed. We show that QC-DWT achieves the best performance for SAR complex image compression. Besides this, in this work, we observed a novel phenomenon that QC-DWT outperforms the zerotree based wavelet coding algorithms, e.g., Consultative Committee for Space Data Systems-Image Data Compression (CCSDS-IDC) and Set Partitioning in Hierarchical Trees algorithm (SPIHT) for SAR complex image data, and there exists deficiency of CCSDS-IDC for SAR complex image data compression. This is because the DWT coefficients of SAR complex image data always have intrascale clustering characteristic and no interscale attenuation characteristic, which is different from that of SAR amplitude images and other optical images.

Highly Scalable Single List Set Partitioning in Hierarchical Trees Image Compression

A highly scalable image compression system produces a compressed image that can be easily decoded at several bit-rates (qualities) and resolutions (sizes). This is a very interesting feature in the heterogeneous environments of the Internet to satisfy the different user's requests. This paper proposes a new algorithm termed Highly Scalable-Single List-SPIHT (HS-SLS). The proposed HS-SLS algorithm produces a highly scalable bit-stream that is both rate and resolution scalable. The flexible bit-stream of the HS-SLS encoder can easily be adapted to various resolution requirements at any bit-rate using a very simple scaling process that is performed on-the-fly and without the need to decode the bit-stream. A unique feature of the new algorithm is that it has low memory requirement and low complexity due to its simple memory management. In addition, the size of the usable memory is fixed and thus can be predefined which avoids the dynamic memory allocation problem. These features make the HS-SLS algorithm very suitable for hardware implementation. The price paid for these valuable features is very slight decrement in the algorithm's performance as compared to the original SPIHT algorithm. Index Terms: Highly Scalable Image Compression, Low Memory Image Compression, Rate scalability, Resolution scalability, Set Partitioning image Compression, SPIHT, Wavelet image compression, Zero-tree coding.

采用超混沌系统的图像压缩加密联合算法

To compress and encrypt images synchronously,ajoint compression-encryption algorithm by using a hyper-chaos Chen system and a improved zero tree coding was proposed.Based on the improved zero-tree coding,a key stream produced by the hyper-chaotic Chen system was used to modify the context and the decision after bit-plane encoding,then they were fed into a MQ arithmetic coder to compress and produce the corresponding code streams.The code streams were feedbacked into the input end of hyper-chaos Chen system to produce a new secret key related to the plaintext and to realize the joint compression-encryption.Experimental results indicate that the Peak Signal to Noise(PSNR)of proposed algorithm is improved at least 1dB,and the key space is 256 bit.The encryption time percentages of total time are all less than 45% and the plaintext and secret have higher sensitivity.The proposed algorithm realizes the synchronization of image compression and encryption,and the image reconstruction quality is higher than that of embedded zero wavelet.It is characterized by the high resistance for linear attack and differential attack,a large key space and better safetyperformance.

An image joint compression-encryption algorithm based on adaptive arithmetic coding

Through a series of studies on arithmetic coding and arithmetic encryption, a novel image joint compression-encryption algorithm based on adaptive arithmetic coding is proposed. The contexts produced in the process of image compression are modified by keys in order to achieve image joint compression encryption. Combined with the bit-plane coding technique, the discrete wavelet transform coefficients in different resolutions can be encrypted respectively with different keys, so that the resolution selective encryption is realized to meet different application needs. Zero-tree coding is improved, and adaptive arithmetic coding is introduced. Then, the proposed joint compression-encryption algorithm is simulated. The simulation results show that as long as the parameters are selected appropriately, the compression efficiency of proposed image joint compression-encryption algorithm is basically identical to that of the original image compression algorithm, and the security of the proposed algorithm is better than the joint encryption algorithm based on interval splitting.

Perceptual Zero-Tree Coding with Efficient Optimization for Embedded Platforms

This study proposes a block-edge-based perceptual zero-tree coding (PZTC) method, which is implemented with efficientoptimization on the embedded platform. PZTC combines two novel compression concepts for coding efficiency and quality:block-edge detection (BED) and the low-complexity and low-memory entropy coder (LLEC). The proposed PZTC wasimplemented as a fixed-point version and optimized on the DSP-based platform based on both the presented platformindependentand platform-dependent optimization technologies. For platform-dependent optimization, this study examinesthe fixed-point PZTC and analyzes the complexity to optimize PZTC toward achieving an optimal coding efficiency.Furthermore, hardware-based platform-dependent optimizations are presented to reduce the memory size. Theperformance, such as compression quality and efficiency, is validated by experimental results.

The Single-Pass Perceptual Embedded Zero-Tree Coding Implementation and Optimization on DSP

In this paper, a block-edge based Single-Pass Perceptual Embedded Zero-tree Coding (SPPEZC) method is proposed and implemented on the DSP-based platform. SPPEZC combines two novel compression concepts which are Block-Edge Detection (BED) and Low-Complexity and Low-Memory Entropy Coder (LLEC) for the coding efficiency and quality. Besides, the proposed SPPEZC is implemented as fixed-point version and optimized on the DSP-based platform based on both the presented platform-independent and platform-dependent optimization technologies. The performance including compression quality and efficiency is validated by experimental results.

基于改进零树编码的图像联合压缩加密算法

On the basis of careful research on arithmetic coding and arithmetic encryption,a kind of image joint compression encryption algorithm based on improved zero-tree coding is proposed,where a key is used to modify the original context and the original decision produced in the process of image compression,and the image joint compression-encryption is realized in this way.In this work,the rule of the context modification and the decision modification algorithm is discussed and its security is analyzed.In order to meet different application requirements,different keys are used to encrypt separately the different wavelet resolution coefficients during bit-plane coding,so the resolution selective encryption is realized.Moreover,the Zero-tree coding was improved,and adaptive arithmetic coding is introduced to the improved Zero-tree coding,and then joint compression-encryption algorithm proposed is simulated.The simulation results show that the proposed image joint compression-encryption algorithm has the same compression efficiency compared with the original image compression algorithm;and the proposed algorithm has better security than the joint compression-encryption algorithm based on the interval splitting.

The single-pass perceptual embedded zero-tree coding implementation on DSP

This paper proposes a block-edge-based Single-Pass Perceptual Embedded Zero-tree Coding (SPPEZC) method. SPPEZC combines two novel compression concepts, called Block-Edge Detection (BED) and the Low-Complexity and Low-Memory Entropy Coder (LLEC), for coding efficiency and quality. Because the edge information can provide beneficial cues for preserving the perceptual quality of compressed images, this paper presents an effective combinative coding scheme, called Single-Pass Perceptual Embedded Zero-tree Coding (SPPEZC), which integrates the improved LLEC and the block-edge information. This approach provides improved perceptual quality in compressed images. Based on the block-edge information, this paper proposes an adaptive architecture for adjusting the quantization table and subsequently coding the quantized coefficients with the LLEC. The proposed SPPEZC approach was implemented and evaluated on both PC-based and DSP-based embedded platforms. Experimental results and comparisons demonstrate that the proposed SPPEZC technique provides computational efficiency as well as satisfactory perceptual quality in compressed images.

A Compression Algorithm in Wireless Sensor Networks of Bearing Monitoring

The energy consumption of wireless sensor networks (WSNs) is always an important problem in the application of wireless sensor networks. This paper proposes a data compression algorithm to reduce amount of data and energy consumption during the data transmission process in the on-line WSNs-based bearing monitoring system. The proposed compression algorithm is based on lifting wavelets, Zerotree coding and Hoffman coding. Among of that, 5/3 lifting wavelets is used for dividing data into different frequency bands to extract signal characteristics. Zerotree coding is applied to calculate the dynamic thresholds to retain the attribute data. The attribute data are then encoded by Hoffman coding to further enhance the compression ratio. In order to validate the algorithm, simulation is carried out by using Matlab. The result of simulation shows that the proposed algorithm is very suitable for the compression of bearing monitoring data. The algorithm has been successfully used in online WSNs-based bearing monitoring system, in which TI DSP TMS320F2812 is used to realize the algorithm.

Geometry Coding for Triangular Mesh Model with Structuring Surrounding Vertices and Connectivity-Oriented Multiresolution Decomposition

In this paper, we propose a novel coding scheme for the geometry of the triangular mesh model. The geometry coding schemes can be classified into two groups: schemes with perfect reconstruction property that maintains their connectivity, and schemes without it in which the remeshing procedure is performed to change the mesh to semi-regular or regular mesh. The former schemes have good coding performance at higher coding rate, while the latter give excellent coding performance at lower coding rate. We propose a geometry coding scheme that maintains the connectivity and has a perfect reconstruction property. We apply a method that successively structures on 2-D plane the surrounding vertices obtained by expanding vertex sequences neighboring the previous layer. Non-separable component decomposition is applied, in which 2-D structured data are decomposed into four components depending on whether their location was even or odd on the horizontal and vertical axes in the 2-D plane. And a prediction and update are performed for the decomposed components. In the prediction process the predicted value is obtained from the vertices, which were not processed, neighboring the target vertex in the 3-D space. And the zero-tree coding is introduced in order to remove the redundancies between the coefficients at similar positions in different resolution levels. SFQ (Space-Frequency Quantization) is applied, which gives the optimal combination of coefficient pruning for the descendant coefficients of each tree element and a uniform quantization for each coefficient. Experiments applying the proposed method to several polygon meshes of different resolutions show that the proposed method gives a better coding performance at lower bit rate when compared to the conventional schemes.

Scalable Intraband and Composite Wavelet-Based Coding of Semiregular Meshes

This paper proposes novel scalable mesh coding designs exploiting the intraband or composite statistical dependencies between the wavelet coefficients. A Laplacian mixture model is proposed to approximate the distribution of the wavelet coefficients. This model proves to be more accurate when compared to commonly employed single Laplacian or generalized Gaussian distribution models. Using the mixture model, we determine theoretically the optimal embedded quantizers to be used in scalable wavelet-based coding of semiregular meshes. In this sense, it is shown that the commonly employed successive approximation quantization is an acceptable, but in general, not an optimal solution. Novel scalable intraband and composite mesh coding systems are proposed, following an information-theoretic analysis of the statistical dependencies between the coefficients. The wavelet subbands are independently encoded using octree-based coding techniques. Furthermore, context-based entropy coding employing either intraband or composite models is applied. The proposed codecs provide both resolution and quality scalability. This lies in contrast to the state-of-the-art interband zerotree-based semiregular mesh coding technique, which supports only quality scalability. Additionally, the experimental results show that, on average, the proposed codecs outperform the interband state-of-the-art for both normal and nonnormal meshes. Finally, compared with a zerotree coding system, the proposed coding schemes are better suited for software/hardware parallelism, due to the independent processing of wavelet subbands.

Triangle Mesh Compression Based on Reverse Subdivision for Mobile Terminals

针对移动用户的实时显示需求,提出一种基于逆细分的三角网格压缩算法.通过改进逆Butterfly简化算法,采用逆改版Loop模式,将细密的三角网格简化生成由稀疏的基网格和一系列偏移量组成的渐进网格;然后,通过设计偏移量小波树,将渐进网格进行嵌入式零树编码压缩.实验结果表明:该算法与以往方法相比,在获得较高压缩比的同时,运行速度较快.适用于几何模型的网络渐进传输和在移动终端上的3D图形实时渲染.;A triangle mesh compression algorithm based on reverse subdivision is introduced. By improving reverse Butterfly simplification algorithm, a mesh simplification algorithm based on reverse Modified Loop scheme is proposed. The dense triangle mesh is decomposed into progressive meshes which consist of a base mesh and a series of displacement wavelets. The progressive meshes are compressed with embedded zerotree coding by constructing displacement wavelets tree structure. The experiments show that the proposed approach is faster and more efficient than previous related techniques. The proposed algorithm can be used for progressive transmission over wireless networks and 3D graphics real-time rendering on mobile terminals.

Hyperspectral Image Compression: Adapting SPIHT and EZW to Anisotropic 3-D Wavelet Coding

Hyperspectral images present some specific characteristics that should be used by an efficient compression system. In compression, wavelets have shown a good adaptability to a wide range of data, while being of reasonable complexity. Some wavelet-based compression algorithms have been successfully used for some hyperspectral space missions. This paper focuses on the optimization of a full wavelet compression system for hyperspectral images. Each step of the compression algorithm is studied and optimized. First, an algorithm to find the optimal 3-D wavelet decomposition in a rate-distortion sense is defined. Then, it is shown that a specific fixed decomposition has almost the same performance, while being more useful in terms of complexity issues. It is shown that this decomposition significantly improves the classical isotropic decomposition. One of the most useful properties of this fixed decomposition is that it allows the use of zero tree algorithms. Various tree structures, creating a relationship between coefficients, are compared. Two efficient compression methods based on zerotree coding (EZW and SPIHT) are adapted on this near-optimal decomposition with the best tree structure found. Performances are compared with the adaptation of JPEG 2000 for hyperspectral images on six different areas presenting different statistical properties.

Effect of Scene Changing on Image Sequences Compression Using Zero Tree Coding

Effect of Scene Changing on Image Sequences Compression Using Zero Tree Coding

Geometric compression of a quadrilateral mesh

A new progressive compression algorithm for arbitrary topology with highly detailed triangle meshes is proposed in this paper. We firstly parameterize the original triangle mesh to a regular quadrilateral approximation. A wavelet transform is then applied to the approximation to remove a large amount of correlation between neighboring vertices. Finally, we used low cost zerotree coding and subdivision based reconstruction to build a sequence of progressive models. Our method will greatly reduce the cost of transportation with acceptable quality loss as shown in the experimental results.

Efficient compression algorithm for hyperspectral images based on correlation coefficients adaptive 3D zerotree coding

The authors propose an efficient compression algorithm for hyperspectral images, which is based on the correlation coefficients adaptive asymmetric tree three-dimensional (3D) set partitioning in hierarchical trees (AT-3DSPIHT) coding of the asymmetric 3D wavelet transform (A3D-DWT) coefficients. According to the characteristics of the correlation coefficients between adjacent spectral bands, a binary tree spectral band grouping algorithm is carried out to divide the adjacent spectral bands into different mode groups. Along with this, an appropriate A3D-DWT with the corresponding decomposition levels and a proper AT-3D zerotree are determined adaptively. Several airborne visible/infrared imaging spectrometer images are used to evaluate the proposed algorithm. Compared with the state-of-the-art 3D-DWT based algorithms, the proposed adaptive AT-3DSPIHT achieves the best compression performance at lower bit rates. Moreover, at the low correlated adjacent bands, the proposed algorithm also outperforms the 2DSPIHT algorithm.

A Hybrid Image Coder Based on SPIHT Algorithm with Embedded Block Coding

Embedded zero-tree coding in wavelet domain has drawn a lot of attention for image compression applications. Among noteworthy zero-tree algorithms is the set partitioning in hierarchical trees (SPIHT) algorithm. For images with textures, high frequency wavelet coefficients are likely to become significant after a few scan passes of SPIHT, and therefore the coding results are often insufficient. It is desirable that the low frequency and high frequency components of an image are coded using different strategies. In this paper, we propose a hybrid algorithm using the SPIHT and EBC (embedded block coding) to code low frequency and high frequency wavelet coefficients, respectively; the intermediate coding results of low frequency coefficients are used to facilitate the coding operation of high frequency coefficients. Experimental results show that the coding performance can be significantly improved by the hybrid SPIHT-EBC algorithm.

Low Bit Rate Video Coding Using DCT-Based Fast Decimation/Interpolation and Embedded Zerotree Coding

In this paper, we propose a low bit rate video coding procedure in the discrete cosine transform (DCT) domain that is based in an embedded zerotree algorithm and uses decimation and interpolation. Theory for decimation/interpolation in the DCT domain is presented, and its implementation with fast algorithms is shown. Motivated by the compositing of several video sources in multipoint video conferencing, we propose a new method for single streams that uses the proposed DCT-decimation in the encoder and the corresponding DCT-interpolation in the decoder. Decimated video frames are encoded using an embedded zerotree and an adaptive arithmetic coder to improve the quality of the decoded videos. Zerotree coding is a progressive coding method that quantizes amplitudes of DCT coefficients of video frames into bit streams with increasing precision. Embedded coding scheme depends on coding a symbol by an adaptive arithmetic encoder as soon as the symbol is obtained by the zerotree coding. Zerotree coder gives flexibility of controlling the bit rate precisely at given target bit rate. Compared to conventional coding our proposed algorithm achieves peak signal-to-noise ratio improvements at low bit rate video coding.

Quantifying the Coding Performance of Zerotrees of Wavelet Coefficients: Degree-$k$ Zerotree

Locating zerotrees in a wavelet transform allows encoding of sets of coefficients with a single symbol. It is an efficient means of coding if the overhead to identify the locations is small compared to the size of the zerotree sets on the average. It is advantageous in this regard to define classes of zerotrees according to the levels from the root until the remainder of the tree contains all zeroes. We call a tree with all zeroes except for the top k levels a degree-k zerotree. A degree-k zerotree coder is one that can encode degree-0 through degree-k zerotrees. We quantify the bit savings of a degree-k2 over a degree-k1, k2>k1, coder. Because SPIHT is a degree-2 zerotree coder and EZW a degree-0 zerotree coder, we are able to explain the superior efficiency of SPIHT. Finally, we gather statistics of degree-k zerotrees for different values of k in the bit planes of several image wavelet transforms to support our analysis of the coding performance of degree-k zerotree coders