Volume 28 | Issue 2

IEEE Transactions on Circuits and Systems for Video Technology covers all aspects of visual information relating to video or that have the potential to impact future developments in the field of video technology and video systems, including but not limited to:

  1. (a) image/video processing: acquisition, representation, display, processing, transform, filtering, enhancement, restoration, watermarking;
  2. (b) image/video analysis and computer vision: characterization, classification, detection, tracking, assessment, segmentation, summarization, understanding, motion estimation, feature extraction, machine learning, machine intelligence, pattern analysis, pattern recognition, neural networks;
  3. (c) image/video compression: quantization, compression, quality assessment, rate control, error resilience, multiview, standards;
  4. (d) image/video communication: coding, streaming, distribution, interaction, networking, transport, wireless and mobile systems;
  5. (e) image/video storage: archives, networks, content management, databases, indexing, search, retrieval;
  6. (f) image/video hardware/software systems: architecture, hardware, software, multiprocessors, parallel processors, algorithms, VLSI, circuits, high-speed, real-time, low-power systems;
  7. (g) image/video applications: synthetic imaging, augmented imaging, video gaming, virtual reality, audio-visual systems, human-computer interaction, multimedia systems, multi-camera systems, surveillance, security, forensics, medical imaging, big data systems, cloud computing, and other video-related technologies.

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Dr. Shipeng Li

Editor in Chief 
Microsoft Research Asia

Feng Wu
Deputy Editor-in-Chief
University of Science and Technology of China


Presents the table of contents for this issue of this publication.

Provides a listing of current staff, committee members and society officers.

Traffic videos are often recorded by vehicle-mounted cameras. Compared with videos recorded by handheld cameras, traffic videos suffer from more challenges, such as higher frequency and more violent jitters, dynamic scenes, large moving objects, and parallax, which can result in significant visual quality degradation. To address these challenges for traffic videos, we propose a special... Read more on IEEE Xplore

We have presented a no-reference quality prediction method for asymmetrically distorted stereoscopic images, which aims to transfer the information from source feature domain to its target quality domain using a label consistent K-singular value decomposition classification framework. To this end, we construct a category-deviation database for dictionary learning that assigns a label for each... Read more on IEEE Xplore

Extracting an object of interest from a single video still faces significant difficulties when the object has variegated appearance, manifests articulated motion, or experiences occlusions by other objects. In this paper, we present a video cosegmentation method to address the aforementioned challenges. Departing from the objectness attributes and motion coherence used by traditional foreground... Read more on IEEE Xplore