(4) For each motion candidates derived from A1, B1, B0, A0, B2, Col and Col2 and numCurrMergeCand is less than 5, if the regular motion candidate is bi-prediction, the motion information from List 0 is added to the TPM merge list (that is, modified to be uni-prediction from List 0) as a new TPM candidate and numCurrMergeCand increased by 1. Such a TPM candidate is named ‘Truncated List0-predicted candidate’.

Full pruning is applied.

(5) For each motion candidates derived from A1, B1, B0, A0, B2, Col and Col2 and numCurrMergeCand is less than 5, if the regular motion candidate is bi-prediction, the motion information from List 1 is added to the TPM merge list (that is, modified to be uni-prediction from List 1) and numCurrMergeCand increased by 1. Such a TPM candidate is named ‘Truncated List1-predicted candidate’.

Full pruning is applied.

(6) For each motion candidates derived from A1, B1, B0, A0, B2, Col and Col2 and numCurrMergeCand is less than 5, if the regular motion candidate is bi-prediction, the motion information of List 1 is firstly scaled to List 0 reference picture, and the average of the two MVs (one is from original List 0, and the other is the scaled MV from List 1) is added to the TPM merge list, such a candidate is called averaged uni-prediction from List 0 motion candidate and numCurrMergeCand increased by 1.

Full pruning is applied.

(7) If numCurrMergeCand is less than 5, zero motion vector candidates are added.

When inserting a candidate to the list, if it has to be compared to all previously added candidates to see whether it is identical to one of them, such a process is called full pruning.