STOL: Spatio-Temporal Online Dictionary Learning for Low Bit-Rate Video Coding

To speed up the convergence rate of learning dictionary in low bit-rate video coding, this paper proposes a spatio-temporal online dictionary learning (STOL) algorithm to improve the original adaptive regularized dictionary learning with K-SVD which involves a high computational complexity and interfere with the coding efficiency. Considering the intrinsic dimensionality of the primitives in training each series of 2-D sub dictionaries is low, the 3-D low-frequency and high-frequency dictionary pair would be formed by the online dictionary learning to update the atoms for optimal sparse representation and convergence. Instead of classical first-order stochastic gradient descent on the constraint set, e.g. K-SVD, the online algorithm would exploit the structure of sparse coding in the design of an optimization procedure in terms of stochastic approximations. It depends on low memory consumption and lower computational cost without the need of explicit learning rate tuning. Through drawing a cubic from i.i.d. samples of a distribution in each inner loop and alternating classical sparse coding steps for computing the decomposition coefficient of the cubic over previous dictionary, the dictionary update problem is converted to solve the expected cost instead of the empirical cost. For dynamic training data over time, online dictionary learning behaves faster than second-order iteration batch alternatives, e.g. K-SVD. Through experiments, the super-resolution reconstruction based on STOL obviously reduces the computational complexity to 40% to 50% of the K-SVD learning-based schemes with a guaranteed accuracy.