Advanced Signal Processing for Mobile Social Media Sharing

用于移动社交媒体共享的高级信号处理

• 批准号: RGPIN-2015-06522
• 负责人: Liang, Jie
• 金额: $ 2.19万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=614194
• 关键词: Advanced; Signal; Processing; Mobile; Social

Recently, sharing images/videos online has become increasingly popular, thanks to advances in wireless networks, smart phones, and social networks. This leads to explosive growth in the number of online images/videos. To reduce uploading cost and delay and the server storage cost, social network providers downsample the high-resolution images/videos before uploading, which wastes the capability of the camera. On the other hand, when other users view these images/videos, they frequently want to zoom in to see some details, but the quality of existing zooming algorithms is still not satisfactory, especially for panoramic images. Therefore, how to improve the mobile social media sharing quality without increasing the cost to social network users and providers is a challenging and important problem. Fortunately, some new theories have been developed, and various powerful cloud computing platforms have become available, offering potential opportunities to address the challenge. The long-term objective of our research is to develop algorithms and systems to improve multimedia communications experience. In the short term, we will develop and integrate novel theories in the following three closely related topics in graph, sparse, and redundant signal processing, apply them to mobile social media sharing, and develop a mobile phone app. 1. Graph transform is a new theory to process data defined on graphs such as those from social networks and sensor networks. It has also been used in image/video coding. However, existing graph transforms do not have fast implementations. In this program, we will design fast graph transforms by generalizing the conventional filter bank theory. 2. Compressed sensing (CS) is a new way to sample and reconstruct sparse signals. Currently, the approximate message passing (AMP) method represents the state of the art in CS. Recently we develop a side information-based AMP (SI-AMP) to incorporate the initial estimate of the signal. In this program, we will further improve SI-AMP by exploiting the latest denoising methods. We will also design a general multi-resolution CS scheme, apply SI-AMP to it, and integrate them with the graph transform. 3. Dictionary-based representation is widely used in CS. In this program, we will design a quantized dictionary-based representation to reduce the storage cost, which is suitable for mobile social media sharing, and apply it to our CS schemes. 4. Mobile app development: we will develop a mobile phone app that allows users to upload and view photos via our app and server. The encoding and decoding of images/videos are based on the algorithms developed above, which will provide better quality than existing schemes. This program will provide solid theoretical training and product development experience to the students involved, so that they can make contributions to Canadian economy in the future.

近来，由于无线网络、智能电话和社交网络的进步，在线共享图像/视频已经变得越来越流行。这导致在线图像/视频数量的爆炸性增长。为了减少上传成本和延迟以及服务器存储成本，社交网络提供商在上传之前对高分辨率图像/视频进行下采样，这浪费了相机的能力。另一方面，当其他用户查看这些图像/视频时，他们经常想要放大以查看一些细节，但现有的缩放算法的质量仍然不尽如人意，尤其是对于全景图像。 因此，如何在不增加社交网络用户和提供商成本的情况下提高移动的社交媒体共享质量是一个具有挑战性的重要问题。幸运的是，一些新的理论已经被开发出来，各种强大的云计算平台已经可用，为应对挑战提供了潜在的机会。 我们研究的长期目标是开发算法和系统，以改善多媒体通信体验。在短期内，我们将开发和整合以下三个密切相关的主题中的图形，稀疏和冗余信号处理的新理论，将其应用于移动的社交媒体共享，并开发移动的手机应用程序。 1.图变换是一种新的理论来处理定义在图上的数据，例如来自社交网络和传感器网络的数据。它也被用于图像/视频编码。然而，现有的图形变换没有快速实现。在这个程序中，我们将通过推广传统的滤波器组理论来设计快速图变换。 2.压缩感知是一种新的稀疏信号采样和重构方法。目前，近似消息传递（AMP）方法代表了CS的最新技术水平。最近，我们开发了一种基于边信息的AMP（SI-AMP），以纳入信号的初始估计。在本计划中，我们将通过利用最新的去噪方法进一步改进SI-AMP。我们还将设计一个通用的多分辨率CS方案，将SI-AMP应用于其中，并将它们与图变换相结合。 3.基于词典的表示方法在CS中得到了广泛的应用。在这个程序中，我们将设计一个量化的基于字典的表示，以减少存储成本，这是适合于移动的社会媒体共享，并将其应用到我们的CS计划。 4.移动的应用程序开发：我们将开发一个移动的手机应用程序，允许用户通过我们的应用程序和服务器上传和查看照片。图像/视频的编码和解码基于上述开发的算法，其将提供比现有方案更好的质量。 该计划将为参与的学生提供坚实的理论培训和产品开发经验，以便他们将来为加拿大经济做出贡献。