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Perception-Based H.264/AVC Video Coding for Resource-Constrained and Low-Bit-Rate Applications
Lih-Jen Kau1, Chin-Kun Tseng1,2,3, Ming-Xian Lee1
1Department of Electronic Engineering, National Taipei University of Technology, Taipei 106344, Taiwan.
Sensors (Basel, Switzerland)
|July 30, 2025
Summary
This study introduces a perception-based video coding algorithm for H.264/AVC, enhancing visual quality in low-bit-rate Internet of Things (IoT) applications. The method prioritizes regions with faces and motion, improving efficiency for edge computing.
Area of Science:
- Computer Vision
- Video Compression
- Signal Processing
Background:
- Efficient video transmission is critical for Internet of Things (IoT) and edge computing due to bandwidth and resource constraints.
- H.264/AVC remains a dominant video compression standard in deployed systems, especially for CCTV surveillance.
- Perception-based video coding is essential for maintaining visual quality while minimizing bit rate and processing overhead.
Purpose of the Study:
- To propose a perception-based video coding algorithm tailored for low-bit-rate H.264/AVC applications.
- To enhance perceptual video quality and optimize resource usage in bandwidth-limited and computationally constrained environments.
- To improve video transmission efficiency for IoT and edge computing systems.
Main Methods:
- Developed a perception-based algorithm focusing on human faces and motion-intensive regions for targeted bit allocation.
- Utilized the Viola-Jones algorithm with AdaBoost for efficient face detection, prioritizing low computational complexity.
- Implemented a dynamic quantization parameter (QP) adjustment strategy to assign finer quantization to regions of interest (ROIs).
Main Results:
- The proposed algorithm achieved superior subjective visual quality and objective Peak Signal-to-Noise Ratio (PSNR) compared to standard methods at the same bit rate.
- Demonstrated effective optimization for low-bit-rate H.264/AVC applications, suitable for embedded systems.
- Showcased a marginal increase in computational complexity, highlighting the algorithm's efficiency.
Conclusions:
- The developed algorithm offers an effective balance between visual quality and computational performance for video transmission in constrained environments.
- The method is well-suited for bandwidth-limited, resource-limited IoT and edge computing applications.
- Prioritizing perceptually important regions significantly improves video quality under low-bit-rate conditions.
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