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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.
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Aggregate shape is classified based on the relative sharpness or roundness of the edges and corners. This classification includes categories like rounded, angular, elongated, and flaky, each with specific characteristics. Rounded aggregates, fully shaped by attrition, are typical of river or seashore gravel, while angular aggregates, such as crushed rock, have well-defined edges. Aggregates that are elongated and flaky are less desirable, as they can reduce the workability and strength of...
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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.
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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,...
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Soft Compression for Lossless Image Coding Based on Shape Recognition.

Gangtao Xin1,2, Pingyi Fan1,2

  • 1Department of Electronic Engineering, Tsinghua University, Beijing 100084, China.

Entropy (Basel, Switzerland)
|December 24, 2021
PubMed
Summary
This summary is machine-generated.

Soft compression, a novel lossless image compression technique, simultaneously reduces coding and spatial redundancy by encoding images using shapes. This method significantly outperforms PNG and JPEG2000, offering substantial savings in bandwidth and storage for various image types.

Keywords:
compressible indicator functionimage set compressioninformation theorylossless image compressionstatistical distributions

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Area of Science:

  • Computer Science
  • Image Processing
  • Data Compression

Background:

  • Traditional image compression methods often struggle to efficiently reduce both coding and spatial redundancy simultaneously.
  • Lossless image compression is crucial for applications where data integrity is paramount, such as medical imaging.

Purpose of the Study:

  • To introduce and analyze a novel soft compression method for lossless image compression.
  • To propose a compressible indicator function for evaluating image compressibility.
  • To compare the performance of soft compression against established standards like PNG and JPEG2000.

Main Methods:

  • Developed a soft compression algorithm that encodes images using shapes to eliminate redundancy.
  • Introduced a compressible indicator function to quantify the average bits required per image location.
  • Investigated and analyzed the algorithm's performance on binary, grayscale, and multi-component images.

Main Results:

  • The soft compression algorithm demonstrated superior compression ratios compared to PNG and JPEG2000 in lossless compression scenarios.
  • The compressible indicator function effectively illustrated the working principle and potential of soft compression.
  • The method is applicable to diverse image types, including binary, grayscale, and multi-component images.

Conclusions:

  • Soft compression offers a significant advancement in lossless image compression technology.
  • The proposed method has the potential to greatly reduce bandwidth and storage requirements for transmitting and storing images.
  • This technique is particularly promising for applications like medical image archiving and transmission.