Existing 3-D point cloud attribute compression methods are rigid for complex signals defined in spatially irregular domain, as they solely depend on geometric information or adopt pre-defined analytic bases for transform. In this article, we propose a multi-scale structured dictionary learning algorithm for 3-D point cloud attribute compression. The proposed multi-scale dictionary leverages hierarchical sparsity within signals by adapting atoms in an arborescent structure. It supports hierarchical sparse transform for encoding voxelized point cloud attributes with progressive refinement of high-frequency details from coarser to finer scales. To represent spatial blocks with varying amounts of occupied voxels, a non-uniform binary weight matrix is incorporated in dictionary learning to characterize the dimensional irregularity of signals. To guarantee optimal approximation with improved efficiency, alternating optimizations including Alternating Direction Method of Multipliers andGauss-Seideliterations are adopted for the weighted mixed$\ell _{2}/\ell _{1}$-minimization. Furthermore, we develop a lossy attribute compression framework by integrating sparse transform based prediction with dedicated quantization and entropy coding routines. Experimental results demonstrate that the proposed framework outperforms the state-of-the-art transform-based coding methods and is competitive with the most recent MPEG PCC test models in point cloud attribute compression.

Reinforcement Learning (RL) based methods optimize evaluation metric directly in image captioning task. In these methods, metric scores of captions are regard as rewards for examples. However, existing methods suffer from inferior performances on hard examples. In this paper, we propose an adaptive hard example mining method with additional supervised training for image captioning. Beam search algorithm is leveraged to estimate score expectation for each example. Examples whose caption scores are lower than expectation are selected automatically. For the selected hard examples, we propose an additional reward policy for high-scoring captions to force model learning from them. The proposed method is hyper-parameter free without tuning. Experimental results on MSCOCO dataset validate effectiveness of the proposed method.

In this paper, we propose a multiscale dictionary learning framework for hierarchical sparse representation of natural images. The proposed framework leverages an adaptive quadtree decomposition to represent structured sparsity in different scales. In dictionary learning, a tree-structured regularized optimization is formulated to distinguish and represent high-frequency details based on varying local statistics and group low-frequency components for local smoothness and structural consistency. In comparison to traditional proximal gradient method, block-coordinate descent is adopted to improve the efficiency of dictionary learning with a guarantee of recovery performance. The proposed framework enables hierarchical sparse representation by naturally organizing the trained dictionary atoms in a prespecified arborescent structure with descending scales from root to leaves. Consequently, the approximation of high-frequency details can be improved with progressive refinement from coarser to finer scales. Employed into image denoising, the proposed framework is demonstrated to be competitive with the state-of-the-art methods in terms of objective and visual restoration quality.

Dictionary learning has emerged as a promising alternative to the conventional hybrid coding framework. However, the rigid structure of sequential training and prediction degrades its performance in scalable video coding. This paper proposes a progressive dictionary learning framework with hierarchical predictive structure for scalable video coding, especially in low bitrate region. For pyramidal layers, sparse representation based on spatio-temporal dictionary is adopted to improve the coding efficiency of enhancement layers with a guarantee of reconstruction performance. The overcomplete dictionary is trained to adaptively capture local structures along motion trajectories as well as exploit the correlations between the neighboring layers of resolutions. Furthermore, progressive dictionary learning is developed to enable the scalability in temporal domain and restrict the error propagation in a closed-loop predictor. Under the hierarchical predictive structure, online learning is leveraged to guarantee the training and prediction performance with an improved convergence rate. To accommodate with the state-of-the-art scalable extension of H.264/AVC and latest High Efficiency Video Coding (HEVC), standardized codec cores are utilized to encode the base and enhancement layers. Experimental results show that the proposed method outperforms the latest scalable extension of HEVC and HEVC simulcast over extensive test sequences with various resolutions.

Classical dictionary learning methods for video coding suffer from high computational 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 3D low-frequency and high-frequency spatio-temporal volumes. In each iteration of the learning process, it randomly selects one sample volume and updates the atoms of dictionary by minimizing the expected cost, rather than optimizes empirical cost over the complete training data, such as batch learning methods, e.g., K-SVD. Since the selected volumes are supposed to be independent identically distributed samples from the underlying distribution, decomposition coefficients attained from the trained dictionary are desirable for sparse representation. Theoretically, it is proved that the proposed STOL could achieve better approximation for sparse representation than K-SVD and maintain both structured sparsity and hierarchical sparsity. It is shown to outperform 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 experiments validate that the STOL-based coding scheme achieves performance improvements than H.264/AVC or High Efficiency Video Coding as well as existing super-resolution-based methods in rate-distortion performance and visual quality.

This paper proposes an adaptive dictionary learning approach based on sub modular optimization. A candidate atom set is constructed based on multiple bases from the combination of analytic and trained dictionaries. With the low-frequency components by the analytic DCT atoms, high-resolution dictionaries can be inferred through online learning to make efficient approximation with rapid convergence. It is formulated as a combinatorial optimization for approximate sub modularity, which is suitable for sparse representation based on dictionaries with arbitrary structures. In single-image super-resolution, the proposed scheme has been demonstrated to improve the reconstruction performance in comparison with double sparsity dictionary in terms of both objective and subjective restoration quality.