High-Dimensional Data Compression Based On Machine Learning Algorithm

Classical dictionary learning methods for video coding suffer from high com-putational complexity and interfered coding efficiency by disregarding its underlying distribution. This paper proposes a spatio-temporal online dictionary learning (STOL) algorithm to speed up the convergence rate of dictionary learning with a guarantee of approximation error. The proposed algorithm incorporates stochastic gradient descents to form a dictionary of pairs of 3-D low-frequency and high-frequency spatio-temporal volumes. In each iteration of the learning process, it randomly selects one sample vol-ume and updates the atoms of dictionary by minimizing the expected cost, rather than optimizes empirical cost over the complete training data like batch learning methods, e.g. K-SVD. Since the selected volumes are supposed to be i.i.d. samples from the underlying distribution, decomposition coefficients attained from the trained dictio-nary are desirable for sparse representation. Theoretically, it is proved that the pro-posed STOL could achieve better approximation for sparse representation than K-SVD and maintain both structured sparsity and hierarchical sparsity. It is shown to outper-form batch gradient descent methods (K-SVD) in the sense of convergence speed and computational complexity, and its upper bound for prediction error is asymptotically equal to the training error. With lower computational complexity, extensive experi-ments validate that the STOL based coding scheme achieve performance improvements than H.264/AVC or HEVC as well as existing super-resolution based methods in rate-distortion performance and visual quality.Meanwhile, based on learning with structured sparsity, this paper proposes a novel multiscale online dictionary learning algorithm with double sparsity structure for qual-ity 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 hierar-chical sparsity. The group sparsity is exploited on lowest sub-band in base layer to obtain the low-frequency sub-dictionary and sparse coefficient. Furthermore, cross-scale decomposition and reconstruction are designed and demonstrated that the recov-ery performance can be guaranteed by an estimation error with an upper bound. The dictionary is updated by stochastic gradient descent to optimize the expected cost rather than empirical cost. Hierarchical high-frequency information is predicted from a pre-learned corresponding sub-dictionary pairs to realize scalable coding. It is witnessed that the proposed algorithm can achieve the SNR scalability in a graceful progressive refinement.To enable learning-based video coding for transmission over heterogenous net-works, this paper proposes a novel temporal scalable video coding framework by pro-gressive dictionary learning. With the hierarchical B-picture prediction structure, the inter-predicted frames would be reconstructed in terms of the spatio-temporal dictio-nary in a successive sense. Within the progressive dictionary learning, the training set is enriched with the samples from the reconstructed frames in the coarse layer. Through minimizing the expected cost, the stochastic gradient descent is leveraged to update the dictionary for practical coding. It is demonstrated that the learning-based scalable framework can effectively guarantee the consistency of motion trajectory with the well-designed spatio-temporal dictionary.