Multiple Linear Regression for High Efficiency Video Intra Coding

In video coding frameworks, the essence of intra coding is leveraging the spatial correlation within a frame to remove redundancy thus achieving compact transmitting data. With modern video acquisition devices improvement, more high-definition videos emerge into people's lives which has set a new challenge for high efficiency video coding. In this paper, we propose a novel intra video coding scheme based on Multiple Linear Regression (MLR), named Multiple linear regression Intra Prediction (MIP). Instead of predicting pixel values by extrapolating, we try to exploit the potential capability of homogeneous regression method. The proposed method has a very concise and neat design yet achieves better performance compared with High Efficiency Video Coding (HEVC) reference software anchor. The experimental results demonstrate the effectiveness of the proposed method and provide interesting insights for further exploiting the capability of conventional algorithms for video coding when many people favor deep learning-based approaches.