2020 Multimedia Prize Paper Award Nomination Period is Open. Any paper published in T-MM in 2017, 2018, or 2019 is eligible. Judging shall be on the bases of originality, subject matter, timeliness, potential impact, and presentation quality.
Recent years have witnessed the rapid development of virtual reality (VR). Above 90% of VR content is in the form of 360° video, also called omnidirectional video or panoramic video. Generally speaking, 360° video offers immersive and interactive viewing experience, as the viewers are able to freely move their heads in the range of 360° × 180° to access different viewports.
Nowadays, 360° video/image has been increasingly popular and drawn great attention. The spherical viewing range of 360° video/image accounts for huge data, which pose the challenges to 360° video/image processing in solving the bottleneck of storage, transmission, etc. Accordingly, the recent years have witnessed the explosive emergence of works on 360° video/image processing.
Panoramic videos are becoming more and more easily obtained for common users. Although these videos have 360∘ field of view, they are usually displayed with perspective views, which needs the saliency informations for viewing angle selection. In this paper, we propose a saliency prediction network for 360∘ videos. Our network takes video frames and optical flows in cube map format as input, thus it does not suffer from image distorations of panoramic frames.
Panoramic videos are becoming more and more easily obtained for common users. Although these videos have 360∘ field of view, they are usually displayed with perspective views, which needs the saliency informations for viewing angle selection. In this paper, we propose a saliency prediction network for 360∘ videos. Our network takes video frames and optical flows in cube map format as input, thus it does not suffer from image distorations of panoramic frames.
This letter proposes a new time domain absorption approach designed to reduce masking components of speech signals under noisy-reverberant conditions. In this method, the non-stationarity of corrupted signal segments is used to detect masking distortions based on a defined threshold.
This letter presents a high resolution method which separates close components of a multi-component linear frequency modulated (LFM) signal and eliminates their Cross-Terms (CTs). We first investigate the energy distribution of the Auto-Terms (ATs) and CTs in ambiguity plane.
This correspondence proposes the use of a real-only equalizer (ROE), which acts on real signals derived from the received offset quadrature amplitude modulation (OQAM) symbols. For the same fading channel, we prove that both ROE and the widely linear equalizer (WLE) yield equivalent outputs.
The cluster of excellence Hearing4all (https://hearing4all.eu/EN/) at the Carl von Ossietzky Universität Oldenburg, Germany, is seeking to fill the position of a
Research Associate (m/f/d)
in the Signal Processing Group (https://uol.de/en/mediphysics-acoustics/sigproc) at the Department of Medical Physics and Acoustics.
Wireless acoustic sensor networks (WASNs) can be used for centralized multi-microphone noise reduction, where the processing is done in a fusion center (FC). To perform the noise reduction, the data needs to be transmitted to the FC. Considering the limited battery life of the devices in a WASN, the total data rate at which the FC can communicate with the different network devices should be constrained.