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Intelligent Sensing Technology for Image and Video Processing

A Special Issue of Sensors (ISSN 1424-8220) belonging to the section "Sensing and Imaging".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 866

Editor


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Guest Editor
Computer Science, Harbin Institute of Technology, Shenzhen, China
Interests: pattern recognition; deep learning; medical biometrics; machine learning

Special Issue Information

Dear Colleagues,

This Special Issue focuses on “Intelligent Sensing Technology for Image and Video Processing”, a cutting-edge interdisciplinary field empowered by the latest advances in large models, intelligent agents and deep learning. As foundation models and autonomous agents reshape visual perception paradigms, intelligent sensing has evolved from traditional data acquisition to cognitive-level understanding, enabling end-to-end visual reasoning, adaptive scene perception and autonomous decision-making for image and video tasks.

This technological shift addresses critical challenges in complex visual scenarios, including cross-modal alignment, real-time dynamic analysis and generalized perception. Covering developing frontiers such as foundation model-driven visual sensing, agent-based intelligent video processing, multi-modal cognitive perception and edge-deployed large model inference, this Special Issue aims to collect state-of-the-art theoretical innovations, algorithm breakthroughs and industrial applications. It serves as a global academic platform to facilitate the integration of large models, agents with intelligent visual sensing, and to accelerate the cognitive intelligence upgrade of image and video processing technologies.

Dr. Jinxing Li
Guest Editor

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Keywords

  • intelligent sensing
  • image processing
  • video processing
  • large models
  • intelligent agent
  • computer vision
  • multi-modal perception
  • cognitive sensing
  • edge intelligence

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Published Papers (1 paper)

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Research

20 pages, 13974 KB  
Article
A Perceptual Rate Control Algorithm Based on JND for Screen Content Video
by Huijie Zheng, Jing Chen and Qi Lin
Sensors 2026, 26(12), 3866; https://doi.org/10.3390/s26123866 - 17 Jun 2026
Viewed by 557
Abstract
The rate control algorithm is designed for natural video by default in video-coding standards. However, computer-generated screen content video (SCV) is very different from natural video captured by a camera, with many different statistical characteristics, such as sharp edges, thin lines, and flat [...] Read more.
The rate control algorithm is designed for natural video by default in video-coding standards. However, computer-generated screen content video (SCV) is very different from natural video captured by a camera, with many different statistical characteristics, such as sharp edges, thin lines, and flat area. This will lead to a difference in the focus of the human visual system (HVS) when viewing on-screen content video. Especially in various sensor data visualization applications such as intelligent display terminals, industrial monitoring and human–computer interaction interfaces, screen content video carries key information collected and reconstructed by image sensors, vision sensors and multimodal sensors. Its edge structures and local details directly affect the interpretation accuracy and application reliability of sensor information. Therefore, it is crucial to investigate perceptual rate control methods that integrate both video content characteristics and human visual perception properties, which possesses substantial theoretical and practical significance. In this paper, we propose a perceptual rate control algorithm for screen content video based on just-noticeable distortion (JND) which is established on the edge profile reconstruction with tolerable variations. First of all, target bit rate allocation for the frame level and CTU level is based on a perceptual weight which is calculated on the JND factor and reconstruction edge character. Secondly, under the constraint of the JND model, an intra rate-distortion (RD) model is established under the constraint of the JND model. The similarity between reference frames and reconstructed frames is taken as feedback in this model. Finally, the proposed rate control algorithm (JND–perceptual rate control (PRC)) is integrated to the existing rate control framework in High-Efficiency Video Coding–Screen Content Coding (HEVC-SCC) for improving the coding efficiency. The experimental results show that the proposed algorithm achieves better bit control precision than the platform, as well as improves the R-D performance of screen content video. In particular, compared with the HEVC-SCC reference software, the coding performance is improved by 3.09 dB on average, the bit rate is saved by 26.51% on average, and the average bit rate mismatch is within 1.159%. Full article
(This article belongs to the Special Issue Intelligent Sensing Technology for Image and Video Processing)
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