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Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
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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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The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
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In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
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Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
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High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
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A Hardware-Friendlyand High-Efficiency H.265/HEVC Encoder for Visual Sensor Networks.

Chi-Ting Ni1, Ying-Chia Huang1, Pei-Yin Chen1

  • 1Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 70101, Taiwan.

Sensors (Basel, Switzerland)
|March 11, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a hardware-friendly algorithm to accelerate High-Efficiency Video Coding (HEVC/H.265) for visual sensor networks. The method significantly reduces encoding time while maintaining high video quality.

Keywords:
HEVCfast CU partitionhardware friendlytexture basedvisual sensor networks

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Area of Science:

  • Computer Vision
  • Video Compression
  • Hardware Acceleration

Background:

  • Visual sensor networks (VSNs) generate large data volumes, posing storage and transmission challenges.
  • High-Efficiency Video Coding (HEVC/H.265) offers high compression but suffers from computational complexity.
  • Existing solutions struggle to balance compression efficiency and processing demands in VSNs.

Purpose of the Study:

  • To develop a hardware-friendly algorithm for accelerating HEVC/H.265 encoding in VSNs.
  • To address the high computational complexity associated with HEVC/H.265 video compression.
  • To improve the efficiency of video data processing for VSN applications.

Main Methods:

  • Proposed a novel algorithm leveraging texture direction and complexity analysis.
  • Implemented optimizations for CU partition skipping and accelerated intra prediction.
  • Focused on intra-frame encoding acceleration for VSN data.

Main Results:

  • Achieved a 45.33% reduction in encoding time compared to HM16.22 under all-intra configuration.
  • Introduced a minimal Bjontegaard delta bit rate (BDBR) increase of only 1.07%.
  • Demonstrated a 53.72% encoding time reduction on six VSN video sequences.

Conclusions:

  • The proposed algorithm offers a high-efficiency solution for HEVC/H.265 acceleration in VSNs.
  • Successfully balances encoding time reduction with minimal impact on video quality (BDBR).
  • Provides a practical approach for managing large visual data streams in sensor networks.