Exploring Contextual Redundancy in Improving Object-Based Video Coding for Video Sensor Networks Surveillance
Exploring Contextual Redundancy in Improving Object-Based Video Coding for Video Sensor Networks Surveillance
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DOI：
10.1109/tmm.2011.2180705
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发表时间：
2012-06
影响因子：
7.3
通讯作者：
T. Tsai;Chung-Yuan Lin
中科院分区：
文献类型：
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作者：
T. Tsai;Chung-Yuan Lin
近年来，智能视频监控试图提供内容分析工具，通过视频传感器网络（VSN）理解和预测动作，实现自动化广域监控。在这个新兴的网络中，视觉对象数据通过不同的设备传输，以适应特定内容分析任务的需求。因此，它们对视频传输提出了新的挑战：如何通过网络高效地将视觉对象数据传输到存储设备、内容分析服务器、远程客户端服务器等各种设备。基于对象的视频编码器可用于减少传输带宽，同时质量损失较小。然而，所涉及的运动补偿技术通常会导致较高的计算复杂度，从而增加VSN的成本。在本文中，探讨了与场景中的背景和前景对象相关的上下文冗余。提出了一种场景分析方法，根据上下文冗余的类型对宏块（MB）进行分类。运动搜索仅针对真正涉及显着运动的特定类型的MB上下文执行。为了便于通过MB上下文进行编码，提出了一种改进的基于对象的编码架构，即双闭环编码器。它以操作率失真优化的方式对 MB 的分类上下文进行编码。实验结果表明，所提出的编码框架可以实现比MPEG-4编码和相关的基于对象的编码方法更高的编码效率，同时显着降低编码复杂度。
In recent years, intelligent video surveillance attempts to provide content analysis tools to understand and predict the actions via video sensor networks (VSN) for automated wide-area surveillance. In this emerging network, visual object data is transmitted through different devices to adapt to the needs of the specific content analysis task. Therefore, they raise a new challenge for video delivery: how to efficiently transmit visual object data to various devices such as storage device, content analysis server, and remote client server through the network. Object-based video encoder can be used to reduce transmission bandwidth with minor quality loss. However, the involved motion-compensated technique often leads to high computational complexity and consequently increases the cost of VSN. In this paper, contextual redundancy associated with background and foreground objects in a scene is explored. A scene analysis method is proposed to classify macroblocks (MBs) by type of contextual redundancy. The motion search is only performed on the specific type of context of MB which really involves salient motion. To facilitate the encoding by context of MB, an improved object-based coding architecture, namely dual-closed-loop encoder, is derived. It encodes the classified context of MB in an operational rate-distortion-optimized sense. The experimental results show that the proposed coding framework can achieve higher coding efficiency than MPEG-4 coding and related object-based coding approaches, while significantly reducing coding complexity.