Conference Agenda

Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).

 
Session Overview
Session
Session 3B: Multimedia Presentation
Time:
Thursday, 05/Jan/2017:
9:00am - 10:20am

Session Chair: Jenny Benois-Pineau
Location: V102
1st floor, 2nd room on left.

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Presentations
9:00am - 9:20am

3D Sound Field Reproduction at Non Central Point for NHK 22.2 System

Song Wang1,2, Ruimin Hu1,2, Shihong Chen1,2, Xiaochen Wang1,2, Yuhong Yang1,2, Weiping Tu1,2, Bo Peng3

1State Key Laboratory of Software Engineering, School of Computer Science, Wuhan University, Wuhan, 430072, China; 2National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University, Wuhan, 430072, China; 3Military Economy Academy, Wuhan, 430072, China

Reducing channel number is convenient for NHK 22.2 system in loudspeaker layout and good for the application of NHK 22.2 in family environment. In 2011, Akio Ando has proposed a down-mixing method which could simplify 22.2 multichannel system to 10.2 and 8.2 multichannel system, but this method only could perfect reproduce 3D sound field at a central listening point. In practice, people may stay at a non central point, Ando's method could not maintain sound physical properties at non central point well. Conventional non central zone sound filed reproduction methods such as pressure matching method and particle velocity matching method have theoretical limitations. This paper propose a general down-mixing method basing on the position of listening point and sound physical properties, it could produce a sweet spot at any non central point in reconstruction field and reduce channel number. In experiments, the proposed method simplifies 22 channel system to 10 channel system, experimental results demonstrate that it performs better than traditional method at non central point sound field reconstruction.


9:20am - 9:40am

Modeling User Performance for Moving Target Selection with a Delayed Mouse

Mark Claypool1, Ragnhild Eg2, Kjetil Raaen2

1Worcester Polytechnic Institute, Worcester, MA, United States of America; 2Westerdals, Oslo, Norway

The growth in networking and cloud services provides opportunities to host multimedia on remote servers, but also brings challenges to developers who must deal with added delays that degrade interactivity. A fundamental action for many integrative multimedia applications is selecting a moving target with the mouse. While previous research has modeled both moving target selection and target selection with delay, there have not been models of moving target selection with delay. Our work presents a user study that measures the effects of delay and target speed on the time to select a moving target with a mouse, with analysis of trends and derivation of a model. The analysis shows delay and speed impact the selection time exponentially and that selection time is well-represented by a model with three terms - two with exponential relationships for delay and speed and one an important interaction term.


9:40am - 10:00am

Single Image Super-resolution with a Parameter Economic Residual-like Convolutional Neural Network

ZE YANG1, Kai Zhang2, Yudong Liang1, Jinjun Wang1

1Xi'an Jiaotong University, China, People's Republic of; 2Harbin Institute of Technology´╝î, China, People's Republic of

Recent years have witnessed great success of convolutional neural network (CNN) for various problems both in low and high level visions. Especially noteworthy is the residual network which was originally proposed to handle high-level vision problems and enjoys several merits. This paper aims to extend the merits of residual network, such as skip connection induced fast training, for a typical low-level vision problem, i.e., single image super-resolution. In general, the two main challenges of existing deep CNN for supper-resolution lie in the gradient exploding/vanishing problem and large amount of parameters or computational cost as CNN goes deeper. Correspondingly, the skip connections or identity mapping shortcuts are utilized to avoid gradient exploding/vanishing problem.

To tackle with the second problem, a parameter economic CNN architecture which has carefully designed width, depth and skip connections was proposed. Experimental results have demonstrated that the proposed CNN model can not only achieve state-of-the-art PSNR and SSIM results for single image super-resolution but also produce visually pleasant results.


10:00am - 10:20am

Color Consistency for Photo Collections without Gamut Problems

Qi-Chong Tian, Laurent D. Cohen

Univ. Paris-Dauphine, PSL Research University

In this paper, we present a color consistency technique to make images in the same collection sharing the color style and not resulting in gamut problems. Some previous methods define simple global parameter-based models and use optimizing algorithms to obtain the unknown parameters, which usually cause gamut problems in bright and dark regions. Our method is based on the range-preserving histogram specifications and can enforce image collections to share color style, without resulting in gamut problems. We divide the input images into two sets having high visual quality and low visual quality respectively. The high visual quality images are used to make color balance. And then the rest low visual quality images are color transferred using the previous corrected high quality images. Our experiments indicate that such histogram-based color correction method is better than the compared algorithm.



 
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