Abstract
This chapter presents the inter prediction tools specified in HEVC. Inter prediction uses available previously reconstructed pictures as reference for motion compensation. Motion compensated prediction generally is the key tool for efficient representation of video content. Changes between successive pictures in the video sequence are approximated by a block-wise displacement between areas in the current picture and previously encoded pictures that are available in the decoded picture buffer at both the encoder and decoder side. The specified precision of the motion information for motion compensation is one quarter-sample for the luma component and one eighth-sample for the chroma components. The implemented advanced motion vector derivation and motion vector prediction methods play a key role in the compression efficiency of HEVC. The sub-sample interpolation filters provide an improved prediction signal at sub-sample locations compared to the interpolation filters of previous video coding specifications. In this chapter, the inter coding tools of HEVC are presented and compared to the inter coding tools which have been available in H.264 | AVC.
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- 1.
The motion vectors are coded as the displacement of the reference block relative to the target location as shown in Fig. 7.1. In this book, the illustration of displacements and prediction directions is otherwise pointing from the prediction source into the target direction, as shown e.g. in Fig. 7.2.
- 2.
In the specification, the final scaling step with rounding is incorporated in the weighted sample prediction processes.
- 3.
Frame 308 of the 1080p sequence BasketballDrive, HM11.0, random access configuration of the JCT-VC common testing conditions [14], \(\text {\,QP}=37\).
- 4.
Frame 250 of the 1080p sequence BasketballDrive, HM11.0, random access configuration of the JCT-VC common testing conditions [14], \(\text {\,QP}=37\).
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Wien, M. (2015). Inter Prediction. In: High Efficiency Video Coding. Signals and Communication Technology. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44276-0_7
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