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Information enters the brain through encoding, which is the input of information into the memory system. Once sensory information is received from the environment, the brain labels or codes it. The information is then organized with similar information and connected to existing concepts. Encoding occurs through automatic processing and effortful processing.
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Related Experiment Video

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High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
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Fast Coding Unit Encoding Mechanism for Low Complexity Video Coding.

Yuan Gao1,2,3, Pengyu Liu1,2,3, Yueying Wu1,2,3

  • 1Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing, China.

Plos One
|March 22, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a new low-complexity coding tree mechanism for High Efficiency Video Coding (HEVC) to speed up encoding. The method significantly reduces encoding time for various coding types, improving overall performance.

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Area of Science:

  • Computer Science
  • Information Technology
  • Digital Signal Processing

Background:

  • High Efficiency Video Coding (HEVC) achieves excellent compression but suffers from high computational complexity.
  • The coding tree structure is a key factor in HEVC's compression efficiency.
  • Reducing encoding time without compromising performance is crucial for HEVC applications.

Purpose of the Study:

  • To propose a novel low-complexity coding tree mechanism for accelerating High Efficiency Video Coding (HEVC) fast coding unit (CU) encoding.
  • To investigate the relationships between CU distribution, Quantization Parameter (QP), and Content Change (CC) in HEVC.
  • To develop a probabilistic model for CU distribution and an update mechanism to handle content variations.

Main Methods:

  • In-depth analysis of CU distribution, QP, and CC.
  • Development of a CU coding tree probability model for predicting CU distribution.
  • Proposal of a CU coding tree probability update mechanism to mitigate model distortion caused by CC.

Main Results:

  • Significant reduction in encoding time: 27% for lossy coding and 42% for visually lossless and lossless coding.
  • The proposed mechanism effectively models and predicts CU distribution.
  • The probability update mechanism successfully addresses distortion issues from content changes.

Conclusions:

  • The novel low-complexity coding tree mechanism substantially reduces HEVC encoding time.
  • The approach improves coding performance across various application conditions.
  • This work offers a practical solution for computationally intensive HEVC encoding.