Quantized Wavelet Coefficients Research Articles

Spatiotemporal wavelet correlogram for human action recognition

In this paper, we present a spatiotemporal wavelet correlogram (STWC) as a new feature for human action recognition (HAR) in videos. The proposed feature benefits from a different approach with respect to bag of visual words, interest point detection and descriptor representation method. The new approach requires neither motion estimation (tracking) nor background/foreground subtraction. STWC is generated more efficiently compared to the state-of-the-art HAR methods and achieves comparable results. STWC utilizes the multi-scale, multi-resolution property of wavelet transform and considers the correlation of wavelet coefficients. It is generated by computing spatiotemporal correlogram of quantized wavelet coefficients. These coefficients are computed using 3D wavelet decomposition and a simple quantization method. Based on the present findings, recommendations are made for the selection of the richest wavelet subbands to compute STWC.

Rapid and coding-efficient SPIHT algorithm for wavelet-based ECG data compression

The set partitioning in hierarchical trees (SPIHT) algorithm is an efficient coding scheme widely used in wavelet-based electrocardiography (ECG) data compression systems. Traditional SPIHT schemes using three lists and a complex sorting process are unsuitable for wearable device design due to their time-consuming nature and low storage efficiency. In this paper, based on a bit-plane representation of quantized wavelet coefficients, a modified SPIHT algorithm is proposed for fast lossless coding. The bit plane is first represented by a tree data structure consisting of two types of primitive trees. The number of primitives is relative to the number of input sampled data. The coding process of the bit-plane data can be regarded as a synthesis that assembles the primitives sequentially based on various decision rules. The assembly rules are further simplified into logic-level decisions in terms of a flag scheme. Using the MIT-BIH arrhythmia database, the experimental results show that the proposed algorithm can achieve an up to 64.35% coding time reduction in comparison with the traditional SPIHT algorithm at a cost of a 0.28% bit-rate increase. The proposed algorithm is simple, regular, and modular.

EMD-based steganography techniques for JPEG2000 encoded images

In JPEG2000 standard, the number of bit planes of wavelet coefficients to be used in encoding is dependent on the compression ratio as well as subbands. These significant wavelet bit planes can be utilized to embed bits of secret data as they are retained in the final bit stream after Tier-2 encoding. In proposed techniques, the above mentioned concept have been utilized to embed secret data bits in lowest significant bit planes of the quantized wavelet coefficients of a cover image. In first technique, secret data is converted into a series of symbols using multiple bases notational system. These bases are selected by using the degree of local variation of coefficient values of the cover image so that coefficient of a complex region can potentially carry more secret data bits as compared to coefficients of smooth region. Symbols of secret data are embedded into bit planes of significant quantized wavelet coefficients by using EMD approaches. In second technique, the secret data bits are embedded into significant quantized wavelet coefficients by using modified EMD. Experimental results show that these proposed techniques provide large embedding capacity and better visual quality stego images than existing steganography techniques applicable to JPEG2000 compressed images. It has also been shown that modified EMD-based technique is better than EMD with multiple bases notational system (MBNS)-based technique.

Multiresolution coding of motion capture data for real-time multimedia applications

In this work, we present a novel and efficient method for coding of motion capture (MoCap) data obtained from recording of human actions. MoCap data is represented as hierarchies of joints and parameterized by translation and rotation of channels or degree-of-freedom (DOF) in a sequence of frames as a function of time. The proposed method approximates the MoCap data of each channel independently using multiresolution discrete wavelet transform (DWT). In order to improve the performance, a skeleton dependent quantization of wavelet coefficients is used that computes a local threshold of each joint based on a global threshold and depth of the joint in the hierarchy of joints. The multiresolution DWT based coding allows to control the bitrate and to decode (reconstruct) the single instance of compressed MoCap data into multiple instances from high to low resolution (quality). We also compared the performance of proposed method with recent and state of the art methods. The proposed method yields smaller storage space and faster encoding time. The method is well suitable for real-time multimedia applications due to its low time and space requirements.

EP-based wavelet coefficient quantization for linear distortion ECG data compression

Reconstruction quality maintenance is of the essence for ECG data compression due to the desire for diagnosis use. Quantization schemes with non-linear distortion characteristics usually result in time-consuming quality control that blocks real-time application. In this paper, a new wavelet coefficient quantization scheme based on an evolution program (EP) is proposed for wavelet-based ECG data compression. The EP search can create a stationary relationship among the quantization scales of multi-resolution levels. The stationary property implies that multi-level quantization scales can be controlled with a single variable. This hypothesis can lead to a simple design of linear distortion control with 3-D curve fitting technology. In addition, a competitive strategy is applied for alleviating data dependency effect. By using the ECG signals saved in MIT and PTB databases, many experiments were undertaken for the evaluation of compression performance, quality control efficiency, data dependency influence. The experimental results show that the new EP-based quantization scheme can obtain high compression performance and keep linear distortion behavior efficiency. This characteristic guarantees fast quality control even for the prediction model mismatching practical distortion curve.

S-EMG signal compression based on domain transformation and spectral shape dynamic bit allocation

BackgroundSurface electromyographic (S-EMG) signal processing has been emerging in the past few years due to its non-invasive assessment of muscle function and structure and because of the fast growing rate of digital technology which brings about new solutions and applications. Factors such as sampling rate, quantization word length, number of channels and experiment duration can lead to a potentially large volume of data. Efficient transmission and/or storage of S-EMG signals are actually a research issue. That is the aim of this work.MethodsThis paper presents an algorithm for the data compression of surface electromyographic (S-EMG) signals recorded during isometric contractions protocol and during dynamic experimental protocols such as the cycling activity. The proposed algorithm is based on discrete wavelet transform to proceed spectral decomposition and de-correlation, on a dynamic bit allocation procedure to code the wavelets transformed coefficients, and on an entropy coding to minimize the remaining redundancy and to pack all data. The bit allocation scheme is based on mathematical decreasing spectral shape models, which indicates a shorter digital word length to code high frequency wavelets transformed coefficients. Four bit allocation spectral shape methods were implemented and compared: decreasing exponential spectral shape, decreasing linear spectral shape, decreasing square-root spectral shape and rotated hyperbolic tangent spectral shape.ResultsThe proposed method is demonstrated and evaluated for an isometric protocol and for a dynamic protocol using a real S-EMG signal data bank. Objective performance evaluations metrics are presented. In addition, comparisons with other encoders proposed in scientific literature are shown.ConclusionsThe decreasing bit allocation shape applied to the quantized wavelet coefficients combined with arithmetic coding results is an efficient procedure. The performance comparisons of the proposed S-EMG data compression algorithm with the established techniques found in scientific literature have shown promising results.

Watermarking Hardware Based on Wavelet Coefficients Quantization Method

Progress in the digital multimedia technologies during the last decade has offered many facilities in the transmission, reproduction, and manipulation of data. However, this advancement has also brought the problem such as copyright protection for content providers. Digital watermarking is a proposed solution for copyright protection for multimedia. The goal of hardware assisted watermarking is to achieve low power usage, real-time performance, reliability, and ease of integration with existing consumer electronic devices. An efficient architecture for transform domain watermarking using quantization approach is proposed here. This architecture is optimized using pipelining. The main objective of this paper is to propose a very-large-scale integration (VLSI) architecture for robust and blind image watermarking chip. Watermarking architectures with and without pipeline are synthesized using Xilinx’s ISE for a field-programmable gate array (FPGA), and then semi-custom integrated chip is designed using UMC 0.18 μm technology standard cell library for both these architectures. The proposed optimized pipelined watermarking encoder core requires 0.027 mm2 area and 0.074 mW power.

Blind image watermarking method based on chaotic key and dynamic coefficient quantization in the DWT domain

In this paper, a new robust watermarking scheme is presented. The proposed algorithm is based on a chaotic mapping and a dynamic blocking, operating in the DWT domain. The framework of the proposed embedding algorithm consists of a special encoding process that has used a chaotic map for producing the embedding key (first key) (in the LL (3) step of the DWT domain). Because of using Arnold’s Cat Map, the key can be produced in the extraction phase without the need for the presence of the original or transmitted information. This phase utilized the special dynamic blocking method and wavelet coefficient quantization (HL (3) or LH (3)) too. The wavelet quantization process sends the sequence (second key) for completion of the extraction phase. Because of the fact that both watermark embedding and detection are accomplished without using the original image, the algorithm claims that it is blind. In the simulation phase, first the approach is examined in the absence of attacks, and then expands the model to allow intentional and unintentional attacks. The experimental result demonstrates that the embedded watermark survives severe image attacks such as adding noise, JPEG compression and median filter. Both the robustness and the security can be well proved by the proposed watermarking algorithm. The robustness of the watermarked image has been verified on the parameters of PSNR and NC.

Enhanced Gabor wavelet correlogram feature for image indexing and retrieval

In this paper, a new feature scheme called enhanced Gabor wavelet correlogram (EGWC) is proposed for image indexing and retrieval. EGWC uses Gabor wavelets to decompose the image into different scales and orientations. The Gabor wavelet coefficients are then quantized using optimized quantization thresholds. In the next step, the autocorrelogram of the quantized wavelet coefficients is computed in each wavelet scale and orientation. Finally, the EGWC index vector simply consists of the autocorrelogram coefficients. Due to non-orthogonality of Gabor decomposition, the resulting wavelet coefficients suffer from redundancy, which increases the computational cost and reduces the effectiveness of EGWC. Here, we present a solution to handle the redundancy problem using non-maximum suppression and adjustment of autocorrelogram distance parameters as a function of the wavelet scale. The retrieval results obtained by applying EGWC to index two image databases with 5,000 natural images and 1,792 texture images demonstrated its better performance in terms of retrieval rates with respect to the state-of-the-art content-based and multidirectional texture indexing algorithms.

Attack on “Watermarking Method Based on Significant Difference of Wavelet Coefficient Quantization”

This letter describes an attack on the recently proposed ldquowatermarking method based on significant difference of wavelet coefficient quantizationrdquo by Lin While the method is shown to be robust against many signal processing operations, security of the watermarking scheme under intentional attack exploiting knowledge of the implementation has been neglected. We demonstrate a straightforward attack which retains the fidelity of the image. The method is therefore not suitable for copyright protection applications. Further, we propose a countermeasure which mitigates the shortcoming.

A blind watermarking method using maximum wavelet coefficient quantization

This paper proposes a blind watermarking algorithm based on maximum wavelet coefficient quantization for copyright protection. The wavelet coefficients are grouped into different block size and blocks are randomly selected from different subbands. We add different energies to the maximum wavelet coefficient under the constraint that the maximum wavelet coefficient is always maximum in a block. The watermark is embedded the local maximum coefficient which can effectively resist attacks. Also, using the block-based watermarking, we can extract the watermark without using the original image or watermark. Experimental results show that the proposed method is quite robust under either non-geometry or geometry attacks.

An Efficient Watermarking Method Based on Significant Difference of Wavelet Coefficient Quantization

This paper proposes a blind watermarking algorithm based on the significant difference of wavelet coefficient quantization for copyright protection. Every seven nonoverlap wavelet coefficients of the host image are grouped into a block. The largest two coefficients in a block are called significant coefficients in this paper and their difference is called significant difference. We quantized the local maximum wavelet coefficient in a block by comparing the significant difference value in a block with the average significant difference value in all blocks. The maximum wavelet coefficients are so quantized that their significant difference between watermark bit 0 and watermark bit 1 exhibits a large energy difference which can be used for watermark extraction. During the extraction, an adaptive threshold value is designed to extract the watermark from the watermarked image under different attacks. We compare the adaptive threshold value to the significant difference which was quantized in a block to determine the watermark bit. The experimental results show that the proposed method is quite effective against JPEG compression, low-pass filtering, and Gaussian noise; the PSNR value of a watermarked image is greater than 40 dB.

Performance Evaluation of A Wavelet-Based ECG Signal Compression Algorithm

The main objective in any data compression algorithm is to provide acceptable reconstruction of the compressed signals at low bit rates. This paper addresses the factors that affect the compression performance of the ECG signal compression algorithm that was presented in “An efficient coding algorithm for the compression of ECG signals using the wavelet transform”. The choice of wavelet filters, decomposition levels, and coefficient quantization is examined and discussed. The experimental results presented in this paper showed the contribution of the above parameters to the compression performance.

Electrocardiogram Compression Method Based on the Adaptive Wavelet Coefficients Quantization Combined to a Modified Two-Role Encoder

An effective method for electrocardiogram (ECG) compression is proposed. It is based on the pyramidal digital wavelet transform. The resulting wavelet coefficients are thresholded iteratively until a user-specified percentage root-mean-square difference is matched. In the next step, the nonzero coefficients of the thresholded vector are quantized adaptively by the linear quantizer of the lowest possible resolution. In the last step, the quantized wavelet coefficients vector is stored efficiently by a new proposed two-role encoder. The obtained results demonstrate clearly the method efficiency and the tradeoff rate-distortion is ensured successfully

Robust Rate-Control for Wavelet-Based Image Coding via Conditional Probability Models

Real-time rate-control for wavelet image coding requires characterization of the rate required to code quantized wavelet data. An ideal robust solution can be used with any wavelet coder and any quantization scheme. A large number of wavelet quantization schemes (perceptual and otherwise) are based on scalar dead-zone quantization of wavelet coefficients. A key to performing rate-control is, thus, fast, accurate characterization of the relationship between rate and quantization step size, the R-Q curve. A solution is presented using two invocations of the coder that estimates the slope of each R-Q curve via probability modeling. The method is robust to choices of probability models, quantization schemes and wavelet coders. Because of extreme robustness to probability modeling, a fast approximation to spatially adaptive probability modeling can be used in the solution, as well. With respect to achieving a target rate, the proposed approach and associated fast approximation yield average percentage errors around 0.5% and 1.0% on images in the test set. By comparison, 2-coding-pass rho-domain modeling yields errors around 2.0%, and post-compression rate-distortion optimization yields average errors of around 1.0% at rates below 0.5 bits-per-pixel (bpp) that decrease down to about 0.5% at 1.0 bpp; both methods exhibit more competitive performance on the larger images. The proposed method and fast approximation approach are also similar in speed to the other state-of-the-art methods. In addition to possessing speed and accuracy, the proposed method does not require any training and can maintain precise control over wavelet step sizes, which adds flexibility to a wavelet-based image-coding system.

High-Performance Wavelet Compression for Mammography: Localization Response Operating Characteristic Evaluation

To evaluate the accuracy of a visually lossless, image-adaptive, wavelet-based compression method for achievement of high compression rates at mammography. The study was approved by the institutional review board of the University of South Florida as a research study with existing medical records and was exempt from individual patient consent requirements. Patient identifiers were obliterated from all images. The study was HIPAA compliant. An algorithm based on scale-specific quantization of biorthogonal wavelet coefficients was developed for the compression of digitized mammograms with high spatial and dynamic resolution. The method was applied to 500 normal and abnormal mammograms from 278 patients who were 32-85 years old, 85 of whom had biopsy-proved cancer. Film images were digitized with a charge-coupled device-based digitizer. The original and compressed reconstructed images were evaluated in a localization response operating characteristic experiment involving three radiologists with 2-10 years of experience in reading mammograms. Compression rates in the range of 14:1 to 2051:1 were achieved, and the rates were dependent on the degree of parenchymal density and the type of breast structure. Ranges of the area under the receiver operating characteristic curve were 0.70-0.83 and 0.72-0.86 for original and compressed reconstructed mammograms, respectively. Ranges of the area under the localization response operating characteristic curve were 0.39-0.65 and 0.43-0.71 for original and compressed reconstructed mammograms, respectively. The localization accuracy increased an average of 6% (0.04 of 0.67) with the compressed mammograms. Localization performance differences were statistically significant with P = .05 and favored interpretation with the wavelet-compressed reconstructed images. The tested wavelet-based compression method proved to be an accurate approach for digitized mammography and yielded visually lossless high-rate compression and improved tumor localization.

Data hiding in the context of lossy compression: a combined approach

We propose a new compression-based algorithm that combines compression using lattice vector quantization (LVQ) in the wavelet domain and data hiding. It deals with two major constraints. (1) compression must not degrade the embedded mark and (2) watermarking must not affect coding performances. Our approach is based on a new multidimensional dead zone that enables us to maintain a sparse representation of the quantized wavelet coefficients, as well as a constant-entropy insertion method. Furthermore, an algorithm to estimate the quantization parameters is proposed. It leads to both variable rate encoding and bit rate adapted data hiding.

Restoration of wavelet-compressed images

We investigate the characteristics of compression noise in images compressed by scalar quantization of the data's wavelet transform coefficients. Such quantization noise is both experimentally and theoretically shown to be spatially varying in the pixel domain, with statistical correlations between the errors at the pixel locations. A quantization covariance matrix is presented that can find use in general restoration scenarios where the observed image or images have been compressed by scalar quantization of image wavelet coefficients. Deblurring is presented as an example use of the quantization model, which demonstrates the model's advantage over the common assumption of independent and identically distributed noise.

Scalable video compression via overcomplete motion compensated wavelet coding

Recently, there have been a flurry of works on overcomplete motion compensated wavelet coding (OMCWC). In this paper, we address the importance of phase and focus on the design of scalable video coding algorithms within the OMCWC framework. Specifically, our new contributions consist of the following three components: (1) efficient block motion estimation techniques in the wavelet domain including hierarchical and fractional-pel block matching, (2) extend overcomplete motion compensated prediction (MCP) into overcomplete motion compensated temporal filtering (MCTF) to achieve temporal scalability (3) context modeling strategies for embedded quantization and entropy coding of 3D wavelet coefficients. Experiment results are used to demonstrate that the class of overomplete MCP/MCTF coders are capable of achieving comparable performance to other competing interframe wavelet coders.

An efficient and scalable low bit-rate video coding with virtual SPIHT

In this paper, we present a wavelet-based hybrid video coding system with spatial and quality scalability. The proposed video codec is based on 2-D wavelet transform, a modified form of set partitioning in hierarchical trees (SPIHT), called virtual SPIHT (VSPIHT) and temporal motion estimation/compensation. The quantisation and encoding of wavelet coefficients are performed by the colour VSPIHT (CVSPIHT) algorithm. The spatial scalability is achieved by re-organising the bit-stream into layers, one for each spatial resolution. The quality scalability is added through locking the motion compensation loop of the encoder and decoder at a fixed rate. This fixed loop rate is less than or equal to the target bit-rate. The scalability in the standard codec like H.263 is achieved by layered approach and each additional layer requires 20–25% extra overhead, whereas our scalable coder requires less than 1% overhead for spatial scalability and negligible overhead for quality scalability. The simulation results demonstrate that the proposed coder outperforms a layered H.263 codec in terms of both objective and subjective quality.