Decoder Derived Cross-Component Linear Model Intra-Prediction for Video Coding

Abstract

This paper presents a decoder derived cross-component linear model (DD-CCLM) intra-prediction method, in which one or more linear models can be used to exploit the similarities between luma and chroma sample values, and the number of linear models used for a specific coding unit is adaptively determined at both encoder and decoder sides in a consistent way, without signalling a syntax element. The neighbouring samples are classified into two or three groups based on a K-means algorithm. Moreover, DDCCLM can be combined with normal intra-prediction modes such as DM mode. The proposed method can be well incorporated with the state-of-the-art CCLM intra-prediction in the Versatile Video Coding standard. Experimental results show that the proposed method provides an overall average bitrate saving of 0.52% for All Intra configurations under the JVET common test conditions, with negligible runtime change. On sequences with rich chroma information, the coding gain is up to 2.07%.