Multiscale Online Dictionary Learning for Quality Scalable Video Coding

Summary form only given. This paper proposes a novel multiscale online dictionary learning algorithm with double sparsity structure for scalable video coding. Along hierarchical structures on the feature set by wavelet transform, the search space of online learning is optimized to sub-blocks for hierarchical sparsity. The group sparsity is exploited on lowest sub-band in the base layer to obtain the low-frequency sub-dictionary and sparse coefficient. We also designed cross-scale decomposition and reconstruction, for which the recovery error can be bounded. The dictionary is updated by stochastic gradient descent to optimize the expected cost. Hierarchical high-frequency information is predicted from a pre-learned corresponding sub-dictionary pairs for scalable coding. We demonstrated that the proposed algorithm can achieve scalable signal to noise ratio (SNR).