# Object-Based Approach for Adaptive Source Coding of Surveillance Video
## 2 Related Works
A previous study [17] modified the relationship between QPs and the rate quantization step size from non-linear to linear mode, and also proposed a complexity-adjustable two-pass rate control scheme based on statistical and theoretical analyses of the quantization scheme. A recent study [18] proposed a perceptual distortion-based RDO video coding scheme for HEVC, in which a new SSIM Lagrange multiplier \(\lambda\) was computed for RDO to decide the optimal coding unit size. At the MB layer, to fit given target bits accurately, the QPs must be adjusted to fit video transmission. A previous study [19] analyzed the relationships among the QPs, MAD, and the coded bits to propose a weighted-window model to reduce computational complexity at the MB layer, which is critical to construct an accurate rate-quantization (R-Q) model that can achieve high bitrates. Another previous study [20] exploited both spatial and temporal correlations among neighboring MBs and proposed a context-adaptive model parameter prediction scheme. This scheme improves estimation accuracy of the MAD of texture with R-Q model-based MB layer rate control for real-time low bitrate applications. However, in high-motion videos, the temporal correlations among the MBs between two contexts cannot provide sufficient information to predict QPs for this R-Q model. It is important to remember that RDO is critical in video compression. A previous study [21] found that R and the Lagrange multiplier \(\lambda\) can provide more robust correspondence than the R-Q model and proposed a \(\lambda\)-domain model based on an R-\(\lambda\) model that did not require complex iterative computation. To reduce the high computational complexity of the rate control algorithm in HEVC. [PERSON], [PERSON] [22] proposed a single-pass joint temporal-quality rate control algorithm. In this algorithm, the predefined target bitrate at each quality layer used in the existing rate control algorithm was not used. Instead, the overall target bitrate adaptively distributed between the quality layers having the same and different temporal resolutions was used. A set of empirical values was first derived to estimate the initial values of the R-D model parameters for the joint temporal and quality layers. Then, a prediction mechanism to update these model parameters during the encoding process was then presented to further improve rate control performance. ROI methods can enhance image quality in target parts of a video while the remaining parts are transmitted at low quality. A previous study [23] used dynamic background modeling to divide MBs into foreground and background regions and proposed strategies to increase the transcoding speed of surveillance video. Another study [24] used a superpixel-based MB selection method to obtain accurate shape information when detecting motion objects using a low bit ROI coding system. This method has been used to monitor road traffic [25]. In addition, an R-\(\lambda\) model has been proposed [26] to present an ROI scheme based on both frame and coding tree unit levels (where QPs are computed independently for different regions) at HEVC standards. Adaptively adjusting the R-Q model to obtain a group of pictures, frame bit allocation, and QP values can enhance ROI video quality. Another proposed method [27] added coefficient \(\omega\) to each frame type and calculated QPs to control the extent to which the ROI was protected, where ROI protection increases as \(\omega\) increases. In addition, a method that uses a ROI with rate control technology to balance video quality and data size has been proposed [28]. Bitstream length and quantization step size can be expressed approximately as a linear function to predict frame-level bit allocation and ROI QP determination. The adaptive updating model uses a linear regression method to update the number of bits of each target frame and a corresponding quantization step size to enhance ROI quality.