# Research on VVC Intra-Frame Bit Allocation Scheme Based on Significance Detection
## 2 Related Work
### Visual Saliency-Based Video Coding  
Visual saliency detection is a fundamental method of image analysis within the domain of computer vision. It operates by employing intelligent algorithms to simulate the attributes of the human visual system to enable the extraction of salient regions from an image. These regions are of particular interest to humans, and their extraction involves the prediction of eye movements and visual gaze points. With the development of related technologies, researchers have gradually combined the field of video coding with saliency detection techniques, actively sacrificing a portion of non-significant video scene regions to achieve better-quality coding results. Currently, the forms of saliency modules combined with video coding techniques are roughly divided into three categories: adjusting the quantization parameter QP and the allocation of target bits in the rate control according to the saliency; replacing the current rate-distortion metrics with distortion metrics based on the characteristics of human vision; and replacing the current rate-distortion metrics with Just Noticeable Distortion (JND) based Perceptual Video Coding (PVC) technique.  
[PERSON] et al. [25] determined that the current R-\(\lambda\) rate control model fails to adequately capture the characteristics of the video content. To address this, they proposed a prominence mapping-based rate control scheme that is applicable at both the framelevel and CTU-level. Additionally, they implemented a new feedback mechanism to improve the correlation between the weights of coding units and the weight gains. This mechanism further influences the frame-level \(\lambda\) and the quantization parameter (QP), resulting in improved performance. [PERSON] et al. [26], in the context of the lack of video coding schemes specifically designed for 360-degree panoramic videos, proposed an adaptive coding solution that improves performance by coding the most visually salient video regions with higher quality in the process of quantization parameter control, and a machine-learning based saliency detection model for detecting higher quality coding applicable to the situation. [PERSON] et al. [27] proposed a saliency map that identifies the key elements of the detector and maps the saliency to the value of the quantization parameter to be used by the video encoder, which improves the accuracy with respect to the default algorithm. A saliency model based on deep learning was suggested by [PERSON] et al. [28] as a means to enhance the effectiveness of video coding. Following the introduction of a saliency-guided preprocessing filtering phase, the two methods are combined. Both objective and subjective assessments indicate that a substantial reduction in bit cost can be achieved without compromising the visual quality. [PERSON] et al. [29] proposed a PVC framework consisting of a fast coding unit (CU) division algorithm and a quantization control algorithm compliant with the VVC optimization scheme in the context of perceptual video coding. First, based on the visual saliency model, a fast CU segmentation scheme is proposed, which includes re-determining the CU segmentation depth by calculating the Scharr operator and variance; and executing the decision for the Intra-Subdivision (ISP) to reduce the coding complexity. Second, a multilevel classification based on significant values at the CU level reduces the bitrate by adjusting the quantization parameters. This algorithm significantly reduces computational complexity and saves bitrate with reasonable peak SNR loss and almost identical subjective perceptual quality.