Fast VVC Intra Encoding for Video Coding for Machines

Traditional video coding technologies compress and reconstruct the video frames, which focus on human perception. However, video coding for machines (VCM) uses the feature stream to bridge the correlation between human perception and machine intelligence for vision tasks. We extract the features for the CU with different shapes with part of resnet architecture for VCM. However, the feature-based methods use the model to complete the forward process, which is very time-consuming for its complex architecture and parameter size. The CU architecture for the feature extraction further increases the operation times. A fast algorithm based on the Histogram of oriented gradient (HOG) is proposed for the video coding for machines with VVC intra to overcome the time-consuming problems while maintaining the performance for the vision tasks with codec. The correlation of the mode decision with the VCM performance is discussed to motivate the fast intra coding for VCM. Moreover, the VTM and VVenc are used to verify the universality of the proposed method. The proposed methods can speed up the fast encoding for 35.21% time saving with 0.26 increment for AP50 for the cityscapes dataset compared with the VTM10.0.