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

2019 ◽  
Vol 9 (1S4) ◽  
pp. 1100-1104
Keyword(s):  
Video Compression ◽  
Side Information ◽  
Analysis Approach ◽  
Compression Rate ◽  
Underwater Video ◽  
Dynamic Textures ◽  
Memory Space ◽  
Minimal Loss ◽  
Multilinear Rank ◽  
Rank Approximation

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.

scholarly journals Low Multilinear Rank Approximation of Tensors and Application in Missing Traffic Data

2014 ◽  
Vol 6 ◽  
pp. 157597 ◽  
Author(s):  
Huachun Tan ◽  
Jianshuai Feng ◽  
Zhengdong Chen ◽  
Fan Yang ◽  
Wuhong Wang
Keyword(s):  
Missing Data ◽  
Gradient Descent ◽  
Tensor Completion ◽  
Volume Data ◽  
Real World Application ◽  
Gradient Descent Algorithm ◽  
Bibliographic Data ◽  
Multilinear Rank ◽  
Rank Approximation ◽  
Weighted Optimization

The problem of missing data in multiway arrays (i.e., tensors) is common in many fields such as bibliographic data analysis, image processing, and computer vision. We consider the problems of approximating a tensor by another tensor with low multilinear rank in the presence of missing data and possibly reconstructing it (i.e., tensor completion). In this paper, we propose a weighted Tucker model which models only the known elements for capturing the latent structure of the data and reconstructing the missing elements. To treat the nonuniqueness of the proposed weighted Tucker model, a novel gradient descent algorithm based on a Grassmann manifold, which is termed Tucker weighted optimization (Tucker-Wopt), is proposed for guaranteeing the global convergence to a local minimum of the problem. Based on extensive experiments, Tucker-Wopt is shown to successfully reconstruct tensors with noise and up to 95% missing data. Furthermore, the experiments on traffic flow volume data demonstrate the usefulness of our algorithm on real-world application.

scholarly journals Lossless 4D Medical Images Compression Using Adaptive Inter Slices Filtering

2018 ◽  
Vol 7 (4) ◽  
pp. 361
Author(s):  
Leila Belhadef ◽  
Zoulikha Mekkakia Maaza
Keyword(s):  
Wavelet Transform ◽  
Video Compression ◽  
Motion Compensation ◽  
Medical Images ◽  
Lossless Compression ◽  
Compression Rate ◽  
New Approach ◽  
Different Dimensions

<p>Recent lossless 4D medical images compression works are based on the application of techniques originated from video compression to efficiently eliminate redundancies in different dimensions of image. In this context we present a new approach of lossless 4D medical images compression which consists to application of 2D wavelet transform in spatial directions followed or not by either lifting transform or motion compensation in inter slices direction, the obtained slices are coded by 3D SPIHT. Our approach was compared with 3D SPIHT with/without motion compensation. The results show our approach offers better performance in lossless compression rate.</p>

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