3-D Dynamic Mesh Compression using Wavelet-Based Multiresolution Analysis

In this paper, we present a wavelet-based progressive compression method for isomorphic 3-D mesh sequence with constant connectivity. Our method reduces the spatial and temporal redundancy by using both spatial and temporal wavelet analysis. To encode geometry information, each mesh frame is decomposed into a base mesh and its spatial wavelet coefficients of each resolution level by spatial wavelet analysis filter bank. The spatially transformed sequence is decomposed into several sub-band signals by temporal wavelet analysis filter bank. The resulting signal is encoded by using an arithmetic coder. Since an isomorphic mesh sequence has the same connectivity over all frames, the connectivity information is encoded only for the first mesh frame. The proposed method enables both progressive representation and lossless compression in a single framework by multi-resolution wavelet analysis with a perfect reconstruction filter bank. Our method is compared with several conventional techniques including our previous work.

Non-blind post-processing algorithm for remote sensing image compression

Hyperspectral imaging (HSI) collects detailed spectral information across hundreds of narrow bands, providing valuable datasets for applications such as medical diagnostics. However, the large size of HSI datasets, often exceeding several gigabytes, creates significant challenges in data transmission, storage, and processing. This study aims to develop a wavelet-based compression method that addresses these challenges while preserving the integrity and quality of spectral information. The proposed method applies wavelet transforms to the spectral dimension of hyperspectral data in three steps: 1) wavelet transformation for dimensionality reduction, 2) spectral cropping to eliminate low-intensity bands, and 3) scale matching to maintain accurate wavelength mapping. Daubechies wavelets were used to achieve up to 32x compression while ensuring spectral fidelity and spatial feature retention. The wavelet-based method achieved up to 32x compression, corresponding to a 96.88% reduction in data size without significant loss of important data. Unlike PCA and ICA, the method preserved the original wavelength scale, enabling straightforward spectral interpretation. Additionally, the compressed data exhibited minimal loss in spatial features, with improvements in contrast and noise reduction compared to spectral binning. This study demonstrates that wavelet-based compression is an effective solution for managing large HSI datasets in medical imaging. The method preserves critical spectral and spatial information, and therefore facilitates efficient data storage and processing, providing the way for practical integration of HSI technology in clinical applications.

Hyperspectral imaging (HSI) collects detailed spectral information across hundreds of narrow bands, providing valuable datasets for applications such as medical diagnostics. However, the large size of HSI datasets, often exceeding several gigabytes, creates significant challenges in data transmission, storage, and processing. We aim to develop a wavelet-based compression method that addresses these challenges while preserving the integrity and quality of spectral information. The proposed method applies wavelet transforms to the spectral dimension of hyperspectral data in three steps: (1)wavelet transformation for dimensionality reduction, (2)spectral cropping to eliminate low-intensity bands, and (3)scale matching to maintain accurate wavelength mapping. Daubechies wavelets were used to achieve up to 32× compression while ensuring spectral fidelity and spatial feature retention. The wavelet-based method achieved up to 32× compression, corresponding to a 96.88% reduction in data size without significant loss of important data. Unlike principal component analysis and independent component analysis, the method preserved the original wavelength scale, enabling straightforward spectral interpretation. In addition, the compressed data exhibited minimal loss in spatial features, with improvements in contrast and noise reduction compared with spectral binning. We demonstrate that wavelet-based compression is an effective solution for managing large HSI datasets in medical imaging. The method preserves critical spectral and spatial information and therefore facilitates efficient data storage and processing, providing a way for the practical integration of HSI technology in clinical applications.

Wavelet-based multi-resolution analysis and artificial neural networks for forecasting temperature and thermal power consumption

In many video applications fractal video compression is use for video coding caused by its different features and lower bit rate. Self similarity concepts of image compression are used in fractal video compression. However selfsimilarity means that fractal picture is consists of duplicates of itself that are interpreted and indicated by a change. More computational complexity is present in fractal video compression for reducing this complexity different technique has been implemented. In video compression, finding the motion vectors (MV) is one of the major factor in motion estimation, due to its high computation complexity allows in between the frames. Many application like multimedia service contains the temporal type of redundancies for emission of video i.e. storage space, bandwidth and transmission cost to reduces this kind of redundancy the motion estimation is used while not degrade a quality of the video. There are number of algorithm has been evolved for fast block based matching techniques in motion estimation to remonstrate the drawbacks relate to diminishing the number of searching point, complexities and computational cost etc., by reason of its effortlessness the block-based technique is demand in motion estimation. Block matching algorithms attracts many researchers from algorithms.the different domain for motion vector estimation also for solving real life applications in motion estimation for video coding. This paper laborite a review of various fractal compression techniques and block matching motion estimation purpose. So, transmission of video takes more time to reach its destination. Therefore, some video compression techniques are involved to remove the redundancy that present in original video. In continuation of fractal image compression uses fractal video compression technique. One of the image compression methods is fractal coding [1]. Its clam is that within a given local region the correlation not only presents in adjacent pixels, but also in global regions or different regions. Mainly video compression technique contains two types of technique i.e. lossy and lossless compression [2]. In lossless technique, reconstruction of total original data is possible. Due to this characteristic, most lossless compression technique referred it for data and executable files etc. But few data may be removed permanently in lossy compression. Mainly two types of redundancies are evolving in sequence of video they are temporal redundancy & spatial redundancy. Spatial redundancies define as correlation present in a frame among neighboring pixel value. Temporal redundancy means by considering a redundancy present in between adjacent frames of images in video. The interframe coding concept uses to lower the temporal redundancy. Similarly, the intraframe coding concept lower the spatial type of redundancy.

B-Mode ultrasound images are degraded by inherent noise called Speckle, which creates a considerable impact on image quality. This noise reduces the accuracy of image analysis and interpretation. Therefore, reduction of speckle noise is an essential task which improves the accuracy of the clinical diagnostics. In this paper, a Multi-directional perfect-reconstruction (PR) filter bank is proposed based on 2-D eigenfilter approach. The proposed method used for the design of two-dimensional (2-D) two-channel linear-phase FIR perfect-reconstruction filter bank. In this method, the fan shaped, diamond shaped and checkerboard shaped filters are designed. The quadratic measure of the error function between the passband and stopband of the filter has been used an objective function. First, the low-pass analysis filter is designed and then the PR condition has been expressed as a set of linear constraints on the corresponding synthesis low-pass filter. Subsequently, the corresponding synthesis filter is designed using the eigenfilter design method with linear constraints. The newly designed 2-D filters are used in translation invariant pyramidal directional filter bank (TIPDFB) for reduction of speckle noise in ultrasound images. The proposed 2-D filters give better symmetry, regularity and frequency selectivity of the filters in comparison to existing design methods. The proposed method is validated on synthetic and real ultrasound data which ensures improvement in the quality of ultrasound images and efficiently suppresses the speckle noise compared to existing methods.

In the process of marketization, the lack of redundancy evaluation of the MSW incineration treatment capacity leads to the regional imbalance of treatment capacity and waste of resources. Therefore, this study aimed to develop a spatial-temporal redundancy evaluation method for the MSW incineration treatment capacity based on the accurate MSW generation prediction using artificial intelligence. To achieve this aim, this study first proposed and finalized a prediction model of the provincial MSW generation by applying the artificial neuron network (ANN) technology and using the statistical data of Jiangsu Province of China from 1990 to 2020. In the finalized model, the input variables consist of three demographic variables, three social variables, and five economic variables; the model structure that includes four hidden layers and 16 neurons in each hidden layer performed best with a coefficient of determination (R2) of 0.995 on the training samples and an R2 of 0.974 on the test set, respectively. Using the finalized model and statistical data of all provinces in China, this study proposed a redundancy evaluation method for the MSW incineration treatment capacity and evaluated the spatial and temporal redundancy status of China. The results first confirm the effectiveness of the proposed method to model and quantify the redundancy problem. Second, according to the evaluation results, even if no new treatment plant will be built before 2025, 10 of China's 31 provinces still have redundancy problems, indicating the severity of this problem. This study first contributes to the body of knowledge by modeling the redundancy problem of the MSW incineration treatment capacity. Moreover, this study provides a tool to quantify temporal and spatial redundancy using advanced technology and publicly available data. Furthermore, the results can help waste-related authorities and organizations make optimal strategies and actions to better match MSW treatment capacity and MSW generation volume.

Construction of urban turbulent flow database with wavelet-based compression: A study with large-eddy simulation of flow and dispersion in block-arrayed building group model

The increasing transistor integration capacity will entail hundreds of processors on a single chip. Further, this will lead to an inherent susceptibility to errors of these systems. To obtain reliable systems again, various redundancy techniques can be applied. Of course, the usage of those techniques involves a significant overhead. Therefore, the identification of the optimal degree of redundancy is an important objective. In this paper we focus on core-level redundancy and checkpointing rollback-recovery. A model to determine the optimal degree of spatial and temporal redundancy regarding the minimal expected execution time will be introduced. Further, we will show that in several cases, the minimal expected execution time is achieved just by a simultaneous combination of both techniques, spatial redundancy and temporal redundancy.

Data Reconciliation via Temporal and Spatial Redundancies

This paper presents a new video compression technique which is based on adaptive vector quantization of multiwavelet coefficients. Three types of redundancies that are common in video sequences are spatial, temporal and psycho visual redundancies. In this work, the spatial redundancy is minimized using Multiwavelet transform, temporal redundancy is minimized using Kite Cross Diamond Search motion estimation algorithm, and the psycho visual redundancy is minimized using adaptive vector quantization technique. The objective of the paper is to develop a low bit rate video coder with acceptable visual quality. The performance of the proposed scheme is compared with wavelet based video coder. Simulation results show that multiwavelet based adaptive vector quantization gives better coding performance than wavelet based adaptive vector quantization scheme.

CHAPTER 9 - Model-based Multiview Video Compression Using Distributed Source Coding Principles

This study proposed a simple and effective response spectrum-compatible ground motions simulation method to mitigate the scarcity of ground motions on seismic hazard analysis base on wavelet-based multi-resolution analysis. The feasibility of the proposed method is illustrated with two recorded ground motions in El Mayor-Cucapah earthquake. The results show that the proposed method enriches the ground motions exponentially. The simulated ground motions agree well with the attenuation characteristics of seismic ground motion without modulating process. Moreover, the pseudo-acceleration response spectrum error between the recorded ground motion and the average of the simulated ground motions is 5.2%, which fulfills the requirement prescribed in Eurocode 8 for artificially simulated ground motions. Besides, the cumulative power spectra between the simulated and recorded ground motions agree well on both high- and low-frequency regions. Therefore, the proposed method offers a feasible alternative in enriching response spectrum-compatible ground motions, especially on the regions with insufficient ground motions.

Current trend in structural condition monitoring system is towards the use of a large number of networked sensors, which correspondingly generate huge amount of sensor data. High data rates pose challenges in data transmission, storage search and remote retrieval, especially for wireless communication network. To address this problem, innovative sensor data compression techniques are needed to reduce the sensor data size. Lossy data compression techniques have the potential to achieve high compression rates but suffer the problem of signal distortion. This paper presents a waveletbased lossy compression method targeted for vibration sensor data. The trade-off between compression rate and signal distortion due to lossy compression is discussed in this paper. The effect of wavelet-based lossy data compression on both the time domain and frequency domain characteristics of vibration signals is studied. Real sensor data collected from a scaled two-story building structure using wireless accelerometer has been used in this study.