At the cost of high complexity

the existing spatial error concealment algorithms may slightly improve the recovery quality by iterative approximation

and some algorithms are optimized only for certain loss patterns. To achieve a trade-off among performance indices

this paper proposes a non-iterative shrinkage multi-directional (NSM) prediction algorithm aiming at handling various types of loss patterns. For the error concealment of the current missing block

the proposed NSM algorithm firstly adopts an isotropic gradient detector to learn the local feature information of the current extrapolation region. Based on a basic concealment unit with 16-pixel neighbor and eight prediction directions

the multi-directional predictor recovers each pixel of the missing block one by one in a shrinkage filling order

and adjusts the weighting coefficients of the predictor according to the availability of adjacent pixels. During the shrinkage filling of a block

different pixel groups are recovered group by group according to their neighbor-level availabilities

and different missing pixels in a pixel group are predicted one by one according to a priori filling rule. Thus

non-iterative reconstruction with low complexity can be realized. Compared with other spatial error concealment algorithms

experimental results show that the proposed NSM algorithm achieved better overall reconstruction performance under various loss patterns

and realized a competitive performance trade-off among versatility

computational complexity

and recovery quality.