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Related Concept Videos

Upsampling01:22

Upsampling

214
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...
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Downsampling01:20

Downsampling

135
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...
135
Deconvolution01:20

Deconvolution

139
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
139
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

63
Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
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Area Computation by the Alternative Coordinate Method01:24

Area Computation by the Alternative Coordinate Method

49
The alternative coordinate method, also known as the Shoelace Formula, is a technique for determining the area of a traverse using Cartesian coordinates. This method relies on the sequential arrangement of x and y coordinates for each point of the shape, ensuring accuracy and ease of application.In this approach, each corner's x and y coordinates are listed as fractions, with the x-coordinate as the numerator and the y-coordinate as the denominator. These coordinates are arranged sequentially...
49
Convolution Properties II01:17

Convolution Properties II

176
The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
176

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A Versatile Point Cloud Compressor Using Universal Multiscale Conditional Coding - Part II: Attribute.

Jianqiang Wang, Ruixiang Xue, Jiaxin Li

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |September 17, 2024
    PubMed
    Summary

    A new framework called Unicorn efficiently compresses point cloud geometry and attributes using multiscale sparse tensors. This learning-based solution offers superior compression efficiency for static and dynamic point clouds in both lossless and lossy modes.

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    Area of Science:

    • Computer Vision
    • Data Compression
    • Machine Learning

    Background:

    • Point cloud data requires efficient compression for storage and transmission.
    • Existing methods like MPEG G-PCC and V-PCC have limitations in compression efficiency.
    • Attribute compression in point clouds presents unique challenges due to varying data characteristics.

    Purpose of the Study:

    • To propose a universal multiscale conditional coding framework, named Unicorn, for point cloud geometry and attribute compression.
    • To develop a versatile, learning-based solution for both static and dynamic point clouds.
    • To achieve state-of-the-art compression efficiency in both lossless and lossy modes.

    Main Methods:

    • Constructing multiscale sparse tensors for voxelized point cloud attribute frames.
    • Processing attribute residuals between lower-scale reconstruction and current-scale data.
    • Leveraging lower-scale spatial priors and temporal reference frames for conditional residual prediction and refinement.

    Main Results:

    • Unicorn significantly outperforms standard-compliant approaches (MPEG G-PCC, V-PCC) and other learning-based solutions.
    • Achieved state-of-the-art compression efficiency for various point cloud types.
    • Demonstrated affordable encoding and decoding runtimes.

    Conclusions:

    • Unicorn provides a versatile and highly efficient solution for point cloud compression.
    • The framework's learning-based approach adapts well to diverse point cloud data.
    • Unicorn sets a new benchmark for compression efficiency in the field.