# Object-Based Approach for Adaptive Source Coding of Surveillance Video
## 1 Introduction  
Network-based video surveillance has become a dominant architecture from the second generation of video surveillance. Intelligent video surveillance as the mainstream of the third generation needs to analyze the network-transmitted videos and detect and recognize objects and events in the videos [1]. The intelligence of the system relies on the high accuracy of detection and recognition, which demands high video quality. Streaming high-quality videos requires significant bandwidth and is not achievable in many surveillance situations, especially for the wireless environment of mobile video surveillance [2]. Wireless networks have an inherent radio signal attenuation problem, which makes guaranteeing appropriate video quality difficult. A source coding method that cannot only compress surveillance videos but also adapt to network situations is crucial. Source coding employs a compression technique to reduce video size, such as frame-type selection [3], macroblock (MB) partition size [4], and quantization parameters (QP) [5; 6]. Optimized selection of I-, P-, and B-frame types through motion estimation can greatly reduce video size. An MB is linear block transform for video compression and is a base unit for motion prediction. In many codecs, MB data are transformed and quantized prior to coding and rescaling. QPs regulate how much spatial information is saved. However, increased picture complexity and object dynamics can induce quality degradation caused by the intrinsic property of video coding. Video streaming with MPEG-based video codingformats has a base coding structure that includes block-based motion compensation. However, this requires more bits to encode motion information. Under a predefined bitrate constraint, video quality declines because some coding parameters, such as QPs, must be adjusted adaptively to satisfy the bitrate constraint. Although the source coding approach has been well studied and applied successfully to improve video streaming efficiency, high-level video characteristics, such as picture complexity and object dynamics, should be incorporated into the source coding scheme to control the bitrate adaptively. Moreover, video quality can degrade significantly when objects move rapidly. In compressed video, rapid movement of large objects induces multiple changes in pixel values between successive frames, which reduces video quality significantly. In addition, the radio signal attenuation associated with wireless networks introduces a higher packet loss rate. Packet loss may occur in the critical building blocks of the coding structure, such as I-slices and I-frames. Packet loss errors can destroy intra MBs and propagate to subsequent video frames. The combination of large object motion and errors inherent in wireless networks reduces video quality dramatically. Figure 1 illustrates such deterioration with respect to object dynamics under wireless and wired network conditions. Two surveillance video frames containing different sizes of objects moving at similar speed are shown in Figure 1a. Here, the network environment is simulated using network simulator version 2 (NS-2) [7]. The peak signal-to-noise ratio (PSNR) shown in in Figure 1a is reduced when object motion occurs. For both moving object events, the reduction is more obvious under wireless conditions. In addition, the larger object (i.e., the car) produces sharper deterioration. The negative effect of object movement relative to speed is shown in Figure 1b. In this video, the same object moves at different speeds, that is, walking and running. A serious decline in PSNR is evident when the object moves at higher speed, particularly under the wireless condition. Providing reliable video quality over networks can be achieved by bitrate control approach that can be classified to be constant bitrate (CBR) and variable bitrate (VBR) controls. Some adaptive methods employ low-level metrics to measure frame complexity and adaptively control bitrates.