Low-rank tensor decomposition and autoencoder (AE) have recently attracted much attention in the hyperspectral anomaly detection field. Although the low-rank tensor decomposition model is self-explainable, a low-rank prior may only partially match real data. In contrast, AE can automatically learn the discriminative features between anomaly and background, whereas AE is not self-explainable. To overcome these problems, a deep low-rank and piecewise-smooth constraint tensor model is proposed for hyperspectral anomaly detection in this paper, which makes full use of the low-rank and piecewise-smooth properties of the background tensor in model-driven tensor-based methods and combines the advantage of data-driven AE methods at the same time. Specifically, the background is reconstructed through the AE network, and the tensor nuclear norm and total variation regularization are designed on the network output to make it self-explainable to exploit the low-rank and piecewise-smooth properties of the background in the three dimensions. At the same time, an alternating direction method of multipliers (ADMM) based approach is given to solve the proposed model, making the low-rank and piecewise-smooth background tensor estimation process and the network training process into two sub-problems. Then, the background can be approximately reconstructed under the constraint of latent priors, while the anomaly is reconstructed with significant reconstruction errors. Finally, the reconstruction errors indicate the anomalous degree. Experimental results validated on several real hyperspectral datasets demonstrate the effectiveness of the proposed algorithm.

As a 3D extension to the High Efficiency Video Coding (HEVC) standard, 3D-HEVC was developed to improve the coding efficiency of multiview videos. It inherits the prediction modes from HEVC, yet both Motion Estimation (ME) and Disparity Estimation (DE) are required for dependent views coding. This improves coding efficiency at the cost of huge computational costs. In this article, an early Merge mode decision approach is proposed for dependent texture views and dependent depth maps coding in 3D-HEVC based on priori and posterior probability models. First, the priori probability model is established by exploiting the hierarchical and interview correlations from those previously encoded blocks. Second, the posterior probability model is built by using the Coded Block Flag (CBF) of the current coding block. Finally, the joint priori and posterior probability model is adopted to early terminate the Merge mode decision for both dependent texture views and dependent depth maps coding. Experimental results show that the proposed approach saves 45.2% and 30.6% encoding time on average for dependent texture views and dependent depth maps coding while maintaining negligible loss of coding efficiency, respectively.

High-efficiency video coding (HEVC) can greatly improve coding efficiency compared with the prior video coding standard H.264/AVC by adopting advanced hierarchical coding structures such as coding unit (CU), prediction unit (PU), and transform unit. For each CU, an exhaustive mode decision strategy is adopted to achieve the best rate distortion (RD) cost, which simultaneously results in enormous computational complexity. In this paper, an early SKIP mode decision algorithm is proposed for the HEVC encoder to speed up the process of mode decision. Each CU size is categorized into either rare used or frequent used by exploiting the correlation of CU depth, which is estimated from the temporally colocated CUs. For the rare-used CU size, the SKIP mode is directly selected as the optimal mode and the remaining mode decision process is early terminated. For the frequent-used CU size, a unimodal stopping model is designed for its early SKIP mode decision by exploiting both hierarchical mode structure and RD cost property. Experimental results show that the proposed early SKIP mode decision method achieves average 58.5% and 54.8% encoding time savings, while the Bjontegaard Delta bit rate only increases average 0.8% and 0.8% for various test sequences under the random access and the low delay B conditions, respectively.