Error Concealment Using Adaptive Multilayer Perceptrons (MLPs) for Block-Based Image Coding

2000 ◽  
Vol 9 (2) ◽  
pp. 83-92 ◽  
Author(s):  
Yu-Len Huang ◽  
Ruey-Feng Chang
Keyword(s):  
Image Coding ◽  
Error Concealment ◽  
Multilayer Perceptrons ◽  
Block Based

High Performance Spatial and Temporal Error-Concealment Algorithms for Block-Based Video Coding Techniques

ETRI Journal ◽  
2005 ◽  
Vol 27 (1) ◽  
pp. 53-63 ◽  
Author(s):  
Ching-Ting Hsu ◽  
Mei-Juan Chen ◽  
Wen-Wei Liao ◽  
Shen-Yi Lo
Keyword(s):  
Video Coding ◽  
High Performance ◽  
Error Concealment ◽  
Block Based ◽  
Temporal Error Concealment

Compression-Dependent Transform-Domain Downward Conversion for Block-Based Image Coding

2018 ◽  
Vol 27 (6) ◽  
pp. 2635-2649 ◽  
Author(s):  
Shuyuan Zhu ◽  
Zhiying He ◽  
Xiandong Meng ◽  
Jiantao Zhou ◽  
Bing Zeng
Keyword(s):  
Image Coding ◽  
Transform Domain ◽  
Block Based

Intra-Predictive Transforms for Block-Based Image Coding

2009 ◽  
Vol 57 (8) ◽  
pp. 3030-3040 ◽  
Author(s):  
Jizheng Xu ◽  
Feng Wu ◽  
Wenjun Zhang
Keyword(s):  
Image Coding ◽  
Block Based

scholarly journals SCENet: Secondary Domain Intercorrelation Enhanced Network for Alleviating Compressed Poisson Noises

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1939 ◽  
Author(s):  
Seok Bong Yoo ◽  
Mikyong Han
Keyword(s):  
Noise Reduction ◽  
Image Coding ◽  
Deep Neural Network ◽  
State Of The Art ◽  
Poisson Noise ◽  
Art Restoration ◽  
Compressed Images ◽  
Coding Systems ◽  
Satisfactory Performance ◽  
Block Based

In real image coding systems, block-based coding is often applied on images contaminated by camera sensor noises such as Poisson noises, which cause complicated types of noises called compressed Poisson noises. Although many restoration methods have recently been proposed for compressed images, they do not provide satisfactory performance on the challenging compressed Poisson noises. This is mainly due to (i) inaccurate modeling regarding the image degradation, (ii) the signal-dependent noise property, and (iii) the lack of analysis on intercorrelation distortion. In this paper, we focused on the challenging issues in practical image coding systems and propose a compressed Poisson noise reduction scheme based on a secondary domain intercorrelation enhanced network. Specifically, we introduced a compressed Poisson noise corruption model and combined the secondary domain intercorrelation prior with a deep neural network especially designed for signal-dependent compression noise reduction. Experimental results showed that the proposed network is superior to the existing state-of-the-art restoration alternatives on classical images, the LIVE1 dataset, and the SIDD dataset.

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