Perception-Based H.264/AVC Video Coding for Resource-Constrained and Low-Bit-Rate Applications

• Published in: Sensors (Basel, Switzerland) Vol. 25; no. 14; p. 4259
• Main Authors: Kau, Lih-Jen, Tseng, Chin-Kun, Lee, Ming-Xian
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 08.07.2025; MDPI
• Subjects: AdaBoost classifier; Algorithms; Bandwidths; Coding standards; Control algorithms; Deep learning; Edge computing; Embedded systems; Evaluation; face detection; H.264/AVC; Image coding; Internet of Things; Jones, Michal; Literature reviews; motion intensity; Quality standards; quantization parameter (QP); Real time; Region of Interest (ROI); Streaming services; Surveillance; Technology application; Video compression; New York; motion intensity; Region of Interest (ROI); AdaBoost classifier; quantization parameter (QP); H.264/AVC; face detection
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s25144259

With the rapid expansion of Internet of Things (IoT) and edge computing applications, efficient video transmission under constrained bandwidth and limited computational resources has become increasingly critical. In such environments, perception-based video coding plays a vital role in maintaining acceptable visual quality while minimizing bit rate and processing overhead. Although newer video coding standards have emerged, H.264/AVC remains the dominant compression format in many deployed systems, particularly in commercial CCTV surveillance, due to its compatibility, stability, and widespread hardware support. Motivated by these practical demands, this paper proposes a perception-based video coding algorithm specifically tailored for low-bit-rate H.264/AVC applications. By targeting regions most relevant to the human visual system, the proposed method enhances perceptual quality while optimizing resource usage, making it particularly suitable for embedded systems and bandwidth-limited communication channels. In general, regions containing human faces and those exhibiting significant motion are of primary importance for human perception and should receive higher bit allocation to preserve visual quality. To this end, macroblocks (MBs) containing human faces are detected using the Viola–Jones algorithm, which leverages AdaBoost for feature selection and a cascade of classifiers for fast and accurate detection. This approach is favored over deep learning-based models due to its low computational complexity and real-time capability, making it ideal for latency- and resource-constrained IoT and edge environments. Motion-intensive macroblocks were identified by comparing their motion intensity against the average motion level of preceding reference frames. Based on these criteria, a dynamic quantization parameter (QP) adjustment strategy was applied to assign finer quantization to perceptually important regions of interest (ROIs) in low-bit-rate scenarios. The experimental results show that the proposed method achieves superior subjective visual quality and objective Peak Signal-to-Noise Ratio (PSNR) compared to the standard JM software and other state-of-the-art algorithms under the same bit rate constraints. Moreover, the approach introduces only a marginal increase in computational complexity, highlighting its efficiency. Overall, the proposed algorithm offers an effective balance between visual quality and computational performance, making it well suited for video transmission in bandwidth-constrained, resource-limited IoT and edge computing environments.