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Related Concept Videos

Upsampling01:22

Upsampling

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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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Random Variables01:09

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A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
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Downsampling01:20

Downsampling

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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.
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Random Sampling Method01:09

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
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Per-Unit Sequence Models01:26

Per-Unit Sequence Models

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An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
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While Mendel’s Law of Segregation states that the two alleles for one gene are separated into different gametes, a different question of how different genes are inherited remains. For example, is the gene for tall plants inherited with the gene for green peas? Mendel asked this question by experimenting with a dihybrid cross; a cross in which both parents are homozygous for two distinct traits resulting in an F1 generation that are heterozygous for both traits.
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Quasi-light Storage for Optical Data Packets
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Multiple Description Coding With Randomly and Uniformly Offset Quantizers.

Lili Meng, Jie Liang, Upul Samarawickrama

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |November 16, 2013
    PubMed
    Summary
    This summary is machine-generated.

    Two new multiple description coding schemes improve image compression efficiency. These methods utilize novel quantization techniques to enhance data reconstruction quality, outperforming existing approaches in lapped transform-based image coding.

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    Area of Science:

    • Signal Processing
    • Information Theory
    • Computer Vision

    Background:

    • Multiple description coding (MDC) is crucial for robust data transmission over unreliable networks.
    • Existing MDC schemes often face trade-offs between compression efficiency and reconstruction quality.
    • Optimizing quantizer design is key to improving MDC performance.

    Purpose of the Study:

    • To develop novel multiple description coding schemes for enhanced image compression.
    • To analyze the theoretical performance of the proposed schemes using random quantization theory.
    • To optimize lapped transforms for image coding applications using the developed theoretical frameworks.

    Main Methods:

    • Development of two MDC schemes: prediction-induced randomly offset quantizers and unequal-deadzone-induced near-uniformly offset quantizers.
    • Predictive coding of source subsets with varying quantization stepsizes.
    • Generalization of random quantization theory to derive closed-form expressions for expected distortion.
    • Application to lapped transform-based multiple description image coding.

    Main Results:

    • Derivation of a closed-form expression for expected distortion in the first scheme and a lower bound for the second.
    • Demonstration of optimal lapped transform parameter selection enabled by closed-form expressions.
    • An iterative algorithm was developed for efficient optimization.
    • Both proposed schemes showed superior performance compared to existing methods in image coding tests.

    Conclusions:

    • The developed MDC schemes offer significant performance improvements in image compression.
    • The theoretical analysis provides a foundation for optimizing MDC systems.
    • The proposed methods are effective for robust image transmission and coding applications.