IEEE TMM Article

You are here

Top Reasons to Join SPS Today!

1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.

IEEE TMM Article

In High Efficiency Video Coding (HEVC), multiple-QP (quantization parameter) optimization can adapt to a local video content. However, the multiple-QP implementation in the HEVC reference software (HM 16.6) achieves the best QP value for each coding block with a large amount of computational complexity.

Recent efforts have been made on acoustic scene classification in the audio signal processing community. In contrast, few studies have been conducted on acoustic scene clustering, which is a newly emerging problem. Acoustic scene clustering aims at merging the audio recordings of the same class of acoustic scene into a single cluster without using prior information and training classifiers. In this study, we propose a method for acoustic scene clustering that jointly optimizes the procedures of feature learning and clustering iteration.

Conventional video saliency detection methods frequently follow the common bottom-up thread to estimate video saliency within the short-term fashion. As a result, such methods can not avoid the obstinate accumulation of errors when the collected low-level clues are constantly ill-detected. Also, being noticed that a portion of video frames, which are not nearby the current video frame over the time axis, may potentially benefit the saliency detection in the current video frame.

Recent advances in image acquisition and analysis have resulted in disruptive innovation in physical rehabilitation systems facilitating cost-effective, portable, video-based gait assessment. While these inexpensive motion capture systems, suitable for home rehabilitation, do not generally provide accurate kinematics measurements on their own, image processing algorithms ensure gait analysis that is accurate enough for rehabilitation programs. 

With the development of cloud storage and privacy protection, reversible data hiding in encrypted images (RDHEI) has attracted increasing attention as a technology that can: embed additional data in the image encryption domain, ensure that the embedded data can be extracted error-free, and the original image can be restored losslessly. 

Image compression has been an important research topic for many decades. Recently, deep learning has achieved great success in many computer vision tasks, and its use in image compression has gradually been increasing. In this paper, we present an energy compaction-based image compression architecture using a convolutional autoencoder (CAE) to achieve high coding efficiency. 

Light field (LF) imaging enables new possibilities for digital imaging, such as digital refocusing, changing of focus plane, changing of viewpoint, scene-depth estimation, and 3D scene reconstruction, by capturing both spatial and angular information of light rays. However, one main problem in dealing with LF data is its sheer volume.

The scalable high efficiency video coding (SHVC) is an extension of high efficiency video coding (HEVC). It introduces multiple layers and inter-layer prediction, thus significantly increases the coding complexity on top of the already complicated HEVC encoder. In inter prediction for quality SHVC, in order to determine the best possible mode at each depth level, a coding tree unit can be recursively split into four depth levels.

This paper deals with the design of a sensing matrix along with a sparse recovery algorithm by utilizing the probability-based prior information for compressed sensing systems. With the knowledge of the probability for each atom of the dictionary being used, a diagonal weighted matrix is obtained and then the sensing matrix is designed by minimizing a weighted function such that the Gram of the equivalent dictionary is as close to the Gram of dictionary as possible.

Research in light field (LF) processing has heavily increased over the last decade. This is largely driven by the desire to achieve the same level of immersion and navigational freedom for camera-captured scenes as it is currently available for CGI content. Standardization organizations such as MPEG and JPEG continue to follow conventional coding paradigms in which viewpoints are discretely represented on 2-D regular grids.

Pages

SPS on Twitter

  • Join SPS President Ahmed Tewfik on Wednesday, 22 September for the IEEE Signal Processing Society Town Hall in conj… https://t.co/31AOCWXvam
  • DEADLINE EXTENDED: The deadline to apply to PROGRESS at ICIP 2021 has been extended to this Thursday, 16 September!… https://t.co/8V2O4lpXr9
  • Voting is now live for the 5-Minute Video Clip Contest! Support SPS students by watching their videos on this year'… https://t.co/PTXiUzRI1u
  • Our newly-formed Synthetic Aperture Standards Committee is now recruiting new members for its initial roster! Check… https://t.co/RcMuQB86kR
  • PROGRESS returns in conjunction with ICIP 2021! Join us 17-18 September for an exciting new program and plenary spe… https://t.co/yJ9rMG73uu

SPS Videos


Signal Processing in Home Assistants

 


Multimedia Forensics


Careers in Signal Processing             

 


Under the Radar