Multimedia Signal Processing and Computer Vision

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 15 May 2026 | Viewed by 627

Special Issue Editors


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Guest Editor
School of Electrical and Data Engineering, University of Technology Sydney, Ultimo, NSW 2007, Australia
Interests: pattern recognition; machine learning; computer vision; multimedia analytics

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Guest Editor
School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100010, China
Interests: infrared; multimodal analytics; MLLM
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Institute of Automation, Chinese Academy of Sciences, Beijing 100010, China
Interests: computer vision; natural language processing; AI applications

Special Issue Information

Dear Colleagues,

This Special Issue, entitled “Multimedia Signal Processing and Computer Vision”, focuses on recent advancements and innovative research in the fields of multimedia signal processing and computer vision. It aims to explore cutting-edge techniques, algorithms, and applications that address the challenges associated with analyzing, interpreting, and enhancing multimedia data, including images, videos, and audio. The scope of this Special Issue includes, but is not limited to, the following topics: deep learning for multimedia analysis, object detection and recognition, image and video compression, 3D vision, augmented and virtual reality, and multimodal data fusion. By compiling contributions from researchers and practitioners, this Special Issue seeks to advance the field of multimedia signal processing and computer vision.

Dr. Haimin Zhang
Dr. Ruiheng Zhang
Dr. Lingxiang Wu
Guest Editors

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Keywords

  • multimedia signal processing
  • computer vision
  • deep learning
  • image and video analysis
  • object detection and recognition
  • multimodal data fusion
  • 3D vision

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Published Papers (1 paper)

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Research

14 pages, 2150 KB  
Article
A Flexible Multi-Core Hardware Architecture for Stereo-Based Depth Estimation CNNs
by Steven Colleman, Andrea Nardi-Dei, Marc C. W. Geilen, Sander Stuijk and Toon Goedemé
Electronics 2025, 14(22), 4425; https://doi.org/10.3390/electronics14224425 - 13 Nov 2025
Viewed by 310
Abstract
Stereo-based depth estimation is becoming more and more important in many applications like self-driving vehicles, earth observation, cartography, robotics and so on. Modern approaches to depth estimation employ artificial intelligence techniques, particularly convolutional neural networks (CNNs). However, stereo-based depth estimation networks involve dual [...] Read more.
Stereo-based depth estimation is becoming more and more important in many applications like self-driving vehicles, earth observation, cartography, robotics and so on. Modern approaches to depth estimation employ artificial intelligence techniques, particularly convolutional neural networks (CNNs). However, stereo-based depth estimation networks involve dual processing paths for left and right input images, which merge at intermediate layers, posing challenges for efficient deployment on modern hardware accelerators. Specifically, modern depth-first and layer-fused execution strategies, which are commonly used to reduce I/O communication and on-chip memory demands, are not readily compatible with such non-linear network structures. To address this limitation, we propose a flexible multi-core hardware architecture tailored for stereo-based depth estimation CNNs. The architecture supports layer-fused execution while efficiently managing dual-path computation and its fusion, enabling improved resource utilization. Experimental results demonstrate a latency reduction of up to 24% compared to state-of-the-art depth-first implementations that do not incorporate stereo-specific optimizations. Full article
(This article belongs to the Special Issue Multimedia Signal Processing and Computer Vision)
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