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Related Concept Videos

Reducing Line Loss01:18

Reducing Line Loss

250
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...
250
Boundary Conditions: Lossless Lines01:21

Boundary Conditions: Lossless Lines

203
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...
203
Lossless Lines01:23

Lossless Lines

241
In electrical engineering, a lossless transmission line is characterized by a purely imaginary propagation constant and a resistive characteristic impedance. The ABCD parameters, which describe the relationship between the input and output voltages and currents, indicate an equivalent π circuit with an imaginary series impedance and a shunt admittance. This results in a transmission line that, when the product of the phase constant (beta) and the length of the line is less than pi, exhibits...
241
Lossy Lines and Overvoltages01:22

Lossy Lines and Overvoltages

153
Transmission-line series resistance and shunt conductance cause three primary effects: attenuation, distortion, and power losses.
Attenuation
When constant series resistance and shunt conductance are present, voltage and current equations are modified. The propagation constant indicates that voltage and current waves consist of both forward and backward traveling components. These waves attenuate as they propagate, with the attenuation factor related to the resistance and conductance. In a...
153
Downsampling01:20

Downsampling

410
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...
410
Mean Absolute Deviation01:13

Mean Absolute Deviation

3.1K
The mean absolute deviation is also a measure of the variability of data in a sample. It is the absolute value of the average difference between the data values and the mean.
Let us consider a dataset containing the number of unsold cupcakes in five shops: 10, 15, 8, 7, and 10. Initially, calculate the sample mean. Then calculate the deviation, or the difference, between each data value and the mean. Next, the absolute values of these deviations are added and divided by the sample size to...
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Learning End-to-End Lossy Image Compression: A Benchmark.

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    This study surveys learned image compression methods, highlighting challenges and opportunities. A novel coarse-to-fine hyperprior model improves rate-distortion performance, especially for high-resolution images.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Signal Processing

    Background:

    • Image compression is fundamental to image and video processing.
    • Traditional methods relied on handcrafted pipelines, while data-driven approaches offer greater flexibility.
    • A systematic benchmark for end-to-end learned image compression is currently lacking.

    Purpose of the Study:

    • To conduct a comprehensive literature survey of learned image compression methods.
    • To analyze challenges and opportunities in the field.
    • To introduce and evaluate a novel hyperprior model for enhanced compression.

    Main Methods:

    • Literature review organized by network architecture, entropy model, and rate control.
    • Development of a coarse-to-fine hyperprior model for entropy estimation and signal reconstruction.
    • Extensive benchmark experiments on CPUs and GPUs.

    Main Results:

    • Identified key aspects for optimizing rate-distortion performance in neural network-based compression.
    • The proposed hyperprior model demonstrates superior rate-distortion performance, particularly for high-resolution images.
    • The model exhibits improved time complexity on both multi-core CPUs and GPUs.

    Conclusions:

    • Learned image compression methods have advanced significantly, driven by data-driven approaches.
    • The developed hyperprior model offers a promising direction for higher-efficiency image compression.
    • The findings provide valuable insights for future research in learned image compression.