Lossless coding method for black-ink signals of high-quality printing images

1998 ◽  
Author(s):  
Shigeo Kato ◽  
Muling Guo ◽  
Madoka Hasegawa
Keyword(s):  
High Quality ◽  
Coding Method ◽  
Lossless Coding

scholarly journals Depth image super-resolution reconstruction based on a modified joint trilateral filter

2019 ◽  
Vol 6 (1) ◽  
pp. 181074 ◽  
Author(s):  
Dongsheng Zhou ◽  
Ruyi Wang ◽  
Xin Yang ◽  
Qiang Zhang ◽  
Xiaopeng Wei
Keyword(s):  
Super Resolution ◽  
Structural Similarity ◽  
Depth Image ◽  
High Quality ◽  
Depth Images ◽  
Trilateral Filter ◽  
Coding Method ◽  
Image Edge ◽  
Image Super Resolution ◽  
Sr Reconstruction

Depth image super-resolution (SR) is a technique that uses signal processing technology to enhance the resolution of a low-resolution (LR) depth image. Generally, external database or high-resolution (HR) images are needed to acquire prior information for SR reconstruction. To overcome the limitations, a depth image SR method without reference to any external images is proposed. In this paper, a high-quality edge map is first constructed using a sparse coding method, which uses a dictionary learned from the original images at different scales. Then, the high-quality edge map is used to guide the interpolation for depth images by a modified joint trilateral filter. During the interpolation, some information of gradient and structural similarity (SSIM) are added to preserve the detailed information and suppress the noise. The proposed method can not only preserve the sharpness of image edge, but also avoid the dependence on database. Experimental results show that the proposed method is superior to some state-of-the-art depth image SR methods.

scholarly journals A Multimodal Lossless Coding Method for Skeletons in Videos

2019 ◽  
Author(s):  
Xiaoyi He ◽  
Mingzhou Liu ◽  
Weiyao Lin ◽  
Xintong Han ◽  
Yanmin Zhu ◽  
...  
Keyword(s):  
Coding Method ◽  
Lossless Coding

scholarly journals Extended Multi WLS Method for Lossless Image Coding

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 919
Author(s):  
Grzegorz Ulacha ◽  
Ryszard Stasiński ◽  
Cezary Wernik
Keyword(s):  
Image Coding ◽  
Constant Component ◽  
Point Of View ◽  
Least Square ◽  
Main Stage ◽  
Current Image ◽  
Coding Method ◽  
Data Compaction ◽  
Lossless Coding ◽  
Arithmetic Coder

In this paper, the most efficient (from data compaction point of view) and current image lossless coding method is presented. Being computationally complex, the algorithm is still more time efficient than its main competitors. The presented cascaded method is based on the Weighted Least Square (WLS) technique, with many improvements introduced, e.g., its main stage is followed by a two-step NLMS predictor ended with Context-Dependent Constant Component Removing. The prediction error is coded by a highly efficient binary context arithmetic coder. The performance of the new algorithm is compared to that of other coders for a set of widely used benchmark images.

scholarly journals Channel Coding of Weighted Probabilistic Model

2021 ◽  
Author(s):  
Jielin Wang
Keyword(s):  
Error Detection ◽  
Channel Coding ◽  
Probability Model ◽  
Binary Sequence ◽  
Ldpc Code ◽  
Correction Method ◽  
Coding Method ◽  
Error Detection And Correction ◽  
Lossless Coding ◽  
Weighted Probability

<p>In this paper a brand new channel error detection and correction method is provided. Artificially adding symbols to the source binary sequence makes the binary sequence present a regularity. The receiver can use this rule to implement error detection and correction. Since many redundant symbols are added, a weighted probability model lossless coding method is proposed in this paper to remove redundant information, and the reasons why Markov chains and conditional probabilities are not feasible are given. It is proven that the method in this paper can reach the channel capacity when the code length approaches infinity. It is shown experimentally that when the code rate is 1/2 in the BIAWGN channel, the method in this paper is superior to the polar code and LDPC code.</p>

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