Content Adaptive Embedded Compression

2017 ◽  
Author(s):  
Yuxiang Shen ◽  
Xiaolin Wu ◽  
Xiao Shu
Keyword(s):  
Embedded Compression ◽  
Content Adaptive

Content adaptive live encoding with open source codecs

2020 ◽  
Author(s):  
Pradeep Ramachandran ◽  
Shushuang Yang ◽  
Praveen Tiwari ◽  
Gopi Satykrishna Akisetty
Keyword(s):  
Open Source ◽  
Content Adaptive

scholarly journals Generalized Reversible Data Hiding with Content-Adaptive Operation and Fast Histogram Shifting Optimization

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 917
Author(s):  
Limengnan Zhou ◽  
Hongyu Han ◽  
Hanzhou Wu
Keyword(s):  
Data Hiding ◽  
Reversible Data Hiding ◽  
Hot Spot ◽  
Data Embedding ◽  
Histogram Shifting ◽  
Secret Data ◽  
Content Selection ◽  
Wide Range ◽  
Map Generation ◽  
Content Adaptive

Reversible data hiding (RDH) has become a hot spot in recent years as it allows both the secret data and the raw host to be perfectly reconstructed, which is quite desirable in sensitive applications requiring no degradation of the host. A lot of RDH algorithms have been designed by a sophisticated empirical way. It is not easy to extend them to a general case, which, to a certain extent, may have limited their wide-range applicability. Therefore, it motivates us to revisit the conventional RDH algorithms and present a general framework of RDH in this paper. The proposed framework divides the system design of RDH at the data hider side into four important parts, i.e., binary-map generation, content prediction, content selection, and data embedding, so that the data hider can easily design and implement, as well as improve, an RDH system. For each part, we introduce content-adaptive techniques that can benefit the subsequent data-embedding procedure. We also analyze the relationships between these four parts and present different perspectives. In addition, we introduce a fast histogram shifting optimization (FastHiSO) algorithm for data embedding to keep the payload-distortion performance sufficient while reducing the computational complexity. Two RDH algorithms are presented to show the efficiency and applicability of the proposed framework. It is expected that the proposed framework can benefit the design of an RDH system, and the introduced techniques can be incorporated into the design of advanced RDH algorithms.

BM3D-HVS: Content-adaptive denoising for improved visual quality

2017 ◽  
Vol 2017 (13) ◽  
pp. 48-55 ◽  
Author(s):  
Karen Egiazarian ◽  
Aram Danielyan ◽  
Nikolay Ponomarenko ◽  
Alessandro Foi ◽  
Oleg Ieremeiev ◽  
...  
Keyword(s):  
Visual Quality ◽  
Adaptive Denoising ◽  
Content Adaptive

Content-Adaptive Region-Based Color Texture Descriptors for Medical Images

2017 ◽  
Vol 21 (1) ◽  
pp. 162-171 ◽  
Author(s):  
Farhan Riaz ◽  
Ali Hassan ◽  
Rida Nisar ◽  
Mario Dinis-Ribeiro ◽  
Miguel Tavares Coimbra
Keyword(s):  
Medical Images ◽  
Content Adaptive ◽  
Color Texture

scholarly journals A Multilayer CARU Framework to Obtain Probability Distribution for Paragraph-Based Sentiment Analysis

2021 ◽  
Vol 11 (23) ◽  
pp. 11344
Author(s):  
Wei Ke ◽  
Ka-Hou Chan
Keyword(s):  
Probability Distribution ◽  
Information Extraction ◽  
Sentiment Analysis ◽  
State Of The Art ◽  
Final Analysis ◽  
The State ◽  
Experimental Results ◽  
Content Adaptive

Paragraph-based datasets are hard to analyze by a simple RNN, because a long sequence always contains lengthy problems of long-term dependencies. In this work, we propose a Multilayer Content-Adaptive Recurrent Unit (CARU) network for paragraph information extraction. In addition, we present a type of CNN-based model as an extractor to explore and capture useful features in the hidden state, which represent the content of the entire paragraph. In particular, we introduce the Chebyshev pooling to connect to the end of the CNN-based extractor instead of using the maximum pooling. This can project the features into a probability distribution so as to provide an interpretable evaluation for the final analysis. Experimental results demonstrate the superiority of the proposed approach, being compared to the state-of-the-art models.

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