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Related Concept Videos

Downsampling01:20

Downsampling

When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
Upsampling01:22

Upsampling

Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next sampling...
Boundary Conditions: Lossless Lines01:21

Boundary Conditions: Lossless Lines

Consider a single-phase, two-wire, lossless transmission line terminated by an impedance at the receiving end and a source with Thevenin voltage and impedance at the sending end. The line, with length, has a surge impedance and wave velocity determined by the line's inductance and capacitance.
At the receiving end, the boundary condition states that the voltage equals the product of the receiving-end impedance and current. This relationship is expressed as a function of the incident and...
Computed Tomography01:10

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
Reducing Line Loss01:18

Reducing Line Loss

In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss in...

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Related Experiment Video

Updated: Jul 7, 2026

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
11:34

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

Published on: December 3, 2013

Low bit-rate efficient compression for seismic data.

A Z Averbuch1, R Meyer, J O Stromberg

  • 1Sch. of Comput. Sci., Tel Aviv Univ., Israel. amir@math.tau.ac.il

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 8, 2008
PubMed
Summary
This summary is machine-generated.

Seismic data compression is crucial due to massive file sizes. Adaptive multiscale local cosine transforms offer higher compression ratios with acceptable noise levels, outperforming traditional wavelet methods for geophysical data.

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Data Processing Methods for 3D Seismic Imaging of Subsurface Volcanoes: Applications to the Tarim Flood Basalt
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Data Processing Methods for 3D Seismic Imaging of Subsurface Volcanoes: Applications to the Tarim Flood Basalt

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High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
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Data Processing Methods for 3D Seismic Imaging of Subsurface Volcanoes: Applications to the Tarim Flood Basalt
07:58

Data Processing Methods for 3D Seismic Imaging of Subsurface Volcanoes: Applications to the Tarim Flood Basalt

Published on: August 7, 2017

Area of Science:

  • Geophysics
  • Data Science
  • Signal Processing

Background:

  • Seismic data files are exceptionally large, often exceeding 10 terabytes, necessitating efficient compression techniques.
  • Traditional seismic data compression methods, like wavelet transforms with Huffman coding, yield artifacts at higher compression ratios.
  • Seismic data possess unique characteristics, including wide dynamic range and coherent noise, differentiating them from standard image data.

Purpose of the Study:

  • To achieve higher compression ratios for seismic data than existing wavelet-based methods.
  • To maintain a comparable level of residual noise, aiming for over 40 dB Signal-to-Noise Ratio (SNR) in decompressed data.
  • To evaluate and introduce novel compression algorithms for seismic data, focusing on performance and processing speed.

Main Methods:

  • Review of established compression algorithms and introduction of new techniques.
  • Application and documentation of compression techniques on diverse seismic datasets.
  • Evaluation of adaptive multiscale local cosine transform with varying window sizes.

Main Results:

  • Adaptive multiscale local cosine transform demonstrated superior performance across all tested seismic datasets.
  • The adaptive transform method outperformed other techniques in terms of Signal-to-Noise Ratio (SNR).
  • Processing speed was a critical factor examined, with some algorithms showing suitability for multimedia compression.

Conclusions:

  • Adaptive multiscale local cosine transform is a highly effective method for seismic data compression.
  • The choice of the best compression method is dataset-dependent.
  • Faster processing speeds are achievable with optimized compression algorithms for seismic data.