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Related Concept Videos

Downsampling01:20

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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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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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Source transformation is a fundamental technique employed in circuit analysis, offering a valuable tool for simplifying complex electrical circuits. This technique involves the replacement of either a voltage source in series with a resistor by a current source in parallel with a resistor, or vice versa. The key concept here is that when the original sources are deactivated (turned off), the equivalent resistance at the circuit's end terminals remains the same.
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A device that transforms voltages from one value to another using induction is called a transformer. A transformer consists of two separate coils, or windings, wrapped around the same soft iron core. However, they are electrically insulated from each other.
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The process of source transformation in the frequency domain entails the conversion of a voltage source, positioned in series with an impedance, into a current source that is parallel to an impedance, or the other way around. It is essential to maintain the following relationships while transitioning from one source type to another.
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The z-transform is a fundamental tool in digital signal processing, enabling the analysis of discrete-time systems through its various properties. It is an invaluable tool for analyzing discrete-time systems, offering a range of properties that simplify complex signal manipulations. One fundamental property is linearity. For any two discrete-time signals, the z-transform of their linear combination equals the same linear combination of their individual z-transforms. This property is essential...
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Visualizing Visual Adaptation
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Syntax-Guided Content-Adaptive Transform for Image Compression.

Yunhui Shi1, Liping Ye1, Jin Wang1

  • 1Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China.

Sensors (Basel, Switzerland)
|August 29, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel syntax-guided image compression framework. It enhances encoding efficiency by better capturing image attributes and reducing redundancies.

Keywords:
adaptive compressiondeep learningimage compression

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Area of Science:

  • Computer Vision
  • Image Processing
  • Data Compression

Background:

  • Increasing image data volume strains storage and transmission.
  • Current learned image compression methods fail to fully exploit pixel correlations.
  • Rate-distortion optimization limits compact attribute representation.

Purpose of the Study:

  • To propose a syntax-guided content-adaptive transform framework for efficient image compression.
  • To improve the capture of image attributes and enhance encoding performance.
  • To address limitations in existing learned image compression techniques.

Main Methods:

  • Developed a syntax-refined side information module to guide adaptive transformations.
  • Incorporated global-local and local-global modules to exploit pixel correlations.
  • Designed upsampling/downsampling modules within codecs to eliminate redundancies.

Main Results:

  • The proposed model adapts to varying image complexities, improving compression efficiency.
  • Demonstrated superior performance across three benchmark datasets.
  • Successfully captured global and local correlations for enhanced encoding.

Conclusions:

  • The syntax-guided content-adaptive transform framework offers enhanced image compression efficiency.
  • The method effectively leverages syntax and side information for adaptive transformations.
  • This approach overcomes limitations of existing methods in representing image attributes.