New weighted prediction architecture for coding scenes with various fading effects image and video processing

Weighted prediction (WP) is one of the new tools in H.264 for encoding scenes with brightness variations. However, a single WP model does not handle all types of brightness variations. Also, large luminance difference induced by object motions would mislead an encoder in its use of WP which results in low coding efficiency. To solve these problems, a picture-based multi-pass encoding strategy, which extensively encodes the same picture multiple times with different WP models and selects the model with the minimum rate-distortion cost, has been adopted in H.264 to obtain better coding performance. However, computational complexity is impractically high. In this paper, a new WP referencing architecture is proposed to facilitate the use of multiple WP models by making a new arrangement of multiple frame buffers in multiple reference frame motion estimation. Experimental results show that the proposed scheme can improve prediction in scenes with different types of brightness variations and considerable luminance difference induced by motions within the same sequence.