Volume 27 | Issue 12

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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Dan Schonfeld
Editor in Chief - 2016 - 2017
ECE Dept. (M/C 154)
University of Illinois at Chicago (UIC)
851 S. Morgan Street – 1020 SEO
Chicago, Illinois

Dr. Shipeng Li
Deputy Editor-in-Chief
Microsoft Research Asia


Researchers have devoted great efforts to image dehazing with prior assumptions in the past decade. Recently developed example-based approaches typically lack elegant models for the hazy process and meanwhile demand synthetic hazy images by manual selection. The priors from observations, and those trained from synthetic images cannot always reflect true structural information of natural images in... Read more on IEEE Xplore

In this paper, we propose a novel matching method to establish dense correspondences automatically between two images in a hierarchical superpixel-to-pixel manner. Our method first estimates dense superpixel pairings between the two images in the coarse-grained level to overcome large patch displacements and then utilizes superpixel level pairings to drive the matchings in the pixel level to... Read more on IEEE Xplore

This paper proposes an effective spatiotemporal saliency model for unconstrained videos with complicated motion and complex scenes. First, superpixel-level motion and color histograms as well as global motion histogram are extracted as the features for saliency measurement. Then a superpixel-level graph with the addition of a virtual background node representing the global motion is constructed,... Read more on IEEE Xplore