# Region-of-Interest Based Coding Scheme for Live Videos
###### Abstract  
In this paper, we introduce a novel rate control scheme specifically tailored for live broadcasting scenarios. Notably, in high-definition live transmissions of sports events and video game competitions that typically exceed 1080 p resolution and run at frame rates of 60 fps or higher, the transcoding speed of encoders often becomes a limiting factor, leading to streams with substantial bitrates but unsatisfactory quality metrics. To enhance the overall Quality of Service (QoS) without increasing the bitrate, it is essential to improve the quality of Regions of Interest (ROI).Our proposed solution presents an ROI-based rate reservoir model that ingeniously leverages Convolutional Neural Networks (CNNs) to predict rate control parameters. This approach aims to optimize the bitrate allocation within high bitrate live broadcasts, thus enhancing the image quality within ROIs. Experimental outcomes demonstrate that this algorithm manages to increase the bitrate by no more than 5%, effectively redistributing the reduced bitrate across the entire Group of Pictures (GOP). As a result, it ensures a gradual decrease in the quality of Regions of Uninterest (ROU), thereby maintaining a balanced quality experience throughout the broadcasted content. video coding; rate control; ROI; live broadcast; high bit rate +
Footnote †: journal: Applied  
0000-0001-9999-4000-0001-9999-4000]X.X.X.Y. The research in [11] presents an ROI-based rate control method specifically designed for High Efficiency Video Coding (HEVC). This approach involves independent processing at the Coding Unit (CU) level for the two regions and applies a larger QP clipping range while adhering to the global bitrate constraint. In [PERSON] et al. 's study [12], a dual encoding system is utilized where a \"basic encoder\" encodes low-resolution full views, and a separate \"ROI encoder\" focuses on high-resolution regions of interest. The bit allocation for the ROI encoder is determined based on the distortion metrics obtained from the corresponding region in the basic encoder. The paper [13] proposes an unequal error protection scheme for compressed HEVC video bitstreams that prioritizes ROI data. Meanwhile, ref. [14] optimizes the quantization parameters for both ROI and ROU source videos by minimizing an objective function, which are then used during the encoding process of the synthesized video. Ref. [15] discusses the ROI location coding tool adopted in the surveillance profile of the AVS2 video coding standard, illustrating three distinct coding schemes: direct-coding, temporal differential-coding, and reconstructed-coding. Ref. [16] realizes a more efficacious distribution of bitrate within machine learning-based image encoders, attenuating the quantization factors in the background regions of the images, thus diminishing the overall level of quantization and consequentially reducing the bitrate assigned to such areas, while concurrently preserving the quality of the ROI sections that are critical for machine learning operations. Ref. [17] proposes a Transformer-based variable-rate image compression system that can achieve variable rate compression with a single model while supporting ROI functionality, introducing a Prompt Generation Network to condition the Transformer Autoencoder's compression process. Lastly, ref. [18] constructs a framework based on FFmpeg and the X.264 codec, integrating a Yolov7 detection model for ROI extraction from video frames, which subsequently generates corresponding masks. This setup prioritizes bitrate allocation to ROI, sacrificing background bitrate allocation in order to maintain overall bitrate stability. The primary aim of rate control in video compression is to attain the lowest possible quality distortion within a predefined bit rate constraint. In this context, rate control commonly integrates Rate-Distortion Optimization (RDO) as a key strategy. To arrive at the most optimized coding mode decision for each Coding Unit (CU), a Lagrange multiplier \(\lambda\) is employed as per Equation (1).