# Object-Based Approach for Adaptive Source Coding of Surveillance Video
## 2 Related Works  
Here, we review source coding techniques that control bitrates for video quality, including the rate-distortion (R-D) model, QP adjustment, region of interest (ROI) and frame layer control methods. Bitrate control methods can be divided into constant bitrate (CBR) and variable bitrate (VBR) approaches. Generally, live streaming broadcasting over the Internet adapts CBR. The sender chooses a constant bitrate to encode and transmit video data, and the receiver does its best to receive video data. However, this approach cannot guarantee video quality because the actual bandwidth or the network transmission quality are other factors. Hence, using CBR to achieve constant quality video transmission is difficult. Fortunately, VBR can be used to adjust bitrate dynamically based on the demanding conditions. Several rate control strategies have been proposed in VBR to provide constant video quality, such as QP parameters adjustment, RD function pre-design, 2-pass optimization design, structural similarity (SSIM)-base analysis and others. Most algorithms use information about group of pictures (GOP), frame layer and macro-block (MB) layer. Adjusting bitrate on the fly based on the actual demanding conditions can help VBR achieve the result of a constant quality video. [PERSON] et al. [13] adopted the VBR approach to adjust image quality. These methods are the hope of achieving consistent visual quality or constant PSNR. In [14], a VBR incremental rate control algorithm to reduce the computational complexity of H264/AVC was proposed. It combined picture complexity estimation and an exponential rate-complexity-quantization model together in the design of a H.264/AVC coding algorithm. This paper also proposes a buffer control method to prevent buffer overflow and underflow by adjusting the quantization parameter. [PERSON] et al. [15] proposed SSIM-motivated perceptual two-pass VBR rate control algorithm for HEVC. They used video quality assessment (VQA) to optimize perceptual video coding protocols. Many rate control schemes that use source coding technology to adjust the bitrate have been proposed for video transmission. Thus, source coding is considered an efficient approach to improve the quality of streaming videos. The R-D model, QP step size determination, and MB size prediction are source coding techniques to enhance the flexibility of rate control and guarantee video quality. In contrast, ROI methods are high-level techniques that employ object dynamics to guarantee quality. A testbed and computed motion activity to achieve a real-time variable frame rate for live video has been proposed [16], and frame layer control, which is considered a low complexity method, can achieve an approximately real-time effect. Motion detection is done, but high-level information, such as moving object, is not. Some methods use frame selection to reduce the overall amount of image transmission, which enhances meaningful information and improves image transmission quality. Another study [11] developed a cooperative framework that involves semi-dynamic environment processing and simple event surveillance. That method used semantic filtering to perform frame selection to adjust frame transmission for back-end monitoring and querying, which was able to achieve better image quality monitoring results over limited bandwidth. Although that method can reduce the number of transmitted images, objects and scenes are restricted. When moving objects appear, it is impossible to determine whether image quality has been enhanced. Many source coding schemes for encoders are based on the R-D model, and bitrate and particularly quantization are important factors in this model. Although the quadratic R-D model is more accurate than the linear R-D model, the quadratic R-D model has higher computational complexity.