Synthesis Analysis Methods for Underwater Video Compression with Tensor Based Minimized Side Information

Synthesis analysis is a common approach used to compress videos with more amounts of dynamic textures. Underwater videos contain more moving species captured by moving camera. These kinds of videos have two types of motion registered by both the species and the camera. In this paper, tensor, an N-way representation of data is used to store the side information obtained from the synthesis analysis approach. The Low multilinear rank approximation (LMLRA) with error correction using residual tensor is applied on the side information to reduce the memory space for side information. The host encoder in synthesis analysis approach plays an important role in providing high compression rate with minimal loss and hence H.265 is used as the host encoder. The results show that the proposed method achieves highest compression ratio with minimal loss due to distortion and saved bit rate which is highly consumed by dynamic textures.

An Efficient Compressive Hyperspectral Imaging Algorithm Based on Sequential Computations of Alternating Least Squares

Hyperspectral imaging is widely used to many applications as it includes both spatial and spectral distributions of a target scene. However, a compression, or a low multilinear rank approximation of hyperspectral imaging data, is required owing to the difficult manipulation of the massive amount of data. In this paper, we propose an efficient algorithm for higher order singular value decomposition that enables the decomposition of a tensor into a compressed tensor multiplied by orthogonal factor matrices. Specifically, we sequentially compute low rank factor matrices from the Tucker-1 model optimization problems via an alternating least squares approach. Experiments with real world hyperspectral imaging revealed that the proposed algorithm could compute the compressed tensor with a higher computational speed, but with no significant difference in accuracy of compression compared to the other tensor decomposition-based compression algorithms.

Hyperspectral Image Denoising Using Global Weighted Tensor Norm Minimum and Nonlocal Low-Rank Approximation

A hyperspectral image (HSI) contains abundant spatial and spectral information, but it is always corrupted by various noises, especially Gaussian noise. Global correlation (GC) across spectral domain and nonlocal self-similarity (NSS) across spatial domain are two important characteristics for an HSI. To keep the integrity of the global structure and improve the details of the restored HSI, we propose a global and nonlocal weighted tensor norm minimum denoising method which jointly utilizes GC and NSS. The weighted multilinear rank is utilized to depict the GC information. To preserve structural information with NSS, a patch-group-based low-rank-tensor-approximation (LRTA) model is designed. The LRTA makes use of Tucker decompositions of 4D patches, which are composed of a similar 3D patch group of HSI. The alternating direction method of multipliers (ADMM) is adapted to solve the proposed models. Experimental results show that the proposed algorithm can preserve the structural information and outperforms several state-of-the-art denoising methods.