image coding
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2022 ◽  
Vol 2022 ◽  
pp. 1-9
Author(s):  
Li Xue ◽  
Chuangjian Yang

In order to improve the effect of copying and recreation of painting works, this paper combines mobile digital multimedia big data technology to improve the image coding algorithm, identify the characteristics of existing works, apply the algorithm to the detailed analysis of painting works, and construct the main functional structure modules of the system. Moreover, this paper combines the existing hardware equipment to construct the painting works’ recreation system and obtains the image processing module. After the system is constructed, the effect of copying and recreating painting works is analyzed through the mobile digital multimedia big data analysis technology. Finally, this paper constructs the system of this paper through simulation methods and uses experiments to calculate the feature recognition effect and copy effect of the painting works of the system. Through experimental analysis, it can be known that the copying and recreation system of painting works based on mobile digital multimedia big data analysis proposed in this paper can help painters effectively improve the effect of recreation.


2021 ◽  
Vol 17 (14) ◽  
pp. 135-153
Author(s):  
Haval Tariq Sadeeq ◽  
Thamer Hassan Hameed ◽  
Abdo Sulaiman Abdi ◽  
Ayman Nashwan Abdulfatah

Computer images consist of huge data and thus require more memory space. The compressed image requires less memory space and less transmission time. Imaging and video coding technology in recent years has evolved steadily. However, the image data growth rate is far above the compression ratio growth, Considering image and video acquisition system popularization. It is generally accepted, in particular that further improvement of coding efficiency within the conventional hybrid coding system is increasingly challenged. A new and exciting image compression solution is also offered by the deep convolution neural network (CNN), which in recent years has resumed the neural network and achieved significant success both in artificial intelligent fields and in signal processing. In this paper we include a systematic, detailed and current analysis of image compression techniques based on the neural network. Images are applied to the evolution and growth of compression methods based on the neural networks. In particular, the end-to-end frames based on neural networks are reviewed, revealing fascinating explorations of frameworks/standards for next-generation image coding. The most important studies are highlighted and future trends even envisaged in relation to image coding topics using neural networks.


Entropy ◽  
2021 ◽  
Vol 23 (12) ◽  
pp. 1680
Author(s):  
Gangtao Xin ◽  
Pingyi Fan

Soft compression is a lossless image compression method that is committed to eliminating coding redundancy and spatial redundancy simultaneously. To do so, it adopts shapes to encode an image. In this paper, we propose a compressible indicator function with regard to images, which gives a threshold of the average number of bits required to represent a location and can be used for illustrating the working principle. We investigate and analyze soft compression for binary image, gray image and multi-component image with specific algorithms and compressible indicator value. In terms of compression ratio, the soft compression algorithm outperforms the popular classical standards PNG and JPEG2000 in lossless image compression. It is expected that the bandwidth and storage space needed when transmitting and storing the same kind of images (such as medical images) can be greatly reduced with applying soft compression.


2021 ◽  
Vol 7 (12) ◽  
pp. 268
Author(s):  
Ryota Motomura ◽  
Shoko Imaizumi ◽  
Hitoshi Kiya

In this paper, we propose a new framework for reversible data hiding in encrypted images, where both the hiding capacity and lossless compression efficiency are flexibly controlled. There exist two main purposes; one is to provide highly efficient lossless compression under a required hiding capacity, while the other is to enable us to extract an embedded payload from a decrypted image. The proposed method can decrypt marked encrypted images without data extraction and derive marked images. An original image is arbitrarily divided into two regions. Two different methods for reversible data hiding in encrypted images (RDH-EI) are used in our method, and each one is used for either region. Consequently, one region can be decrypted without data extraction and also losslessly compressed using image coding standards even after the processing. The other region possesses a significantly high hiding rate, around 1 bpp. Experimental results show the effectiveness of the proposed method in terms of hiding capacity and lossless compression efficiency.


2021 ◽  
Vol 15 (2) ◽  
pp. 149-163
Author(s):  
Sahlah Abd Ali Al-hamdanee ◽  
Eman Abd Elaziz ◽  
Khalil Alsaif
Keyword(s):  

2021 ◽  
Vol 7 (11) ◽  
pp. 244
Author(s):  
Alan Sii ◽  
Simying Ong ◽  
KokSheik Wong

JPEG is the most commonly utilized image coding standard for storage and transmission purposes. It achieves a good rate–distortion trade-off, and it has been adopted by many, if not all, handheld devices. However, often information loss occurs due to transmission error or damage to the storage device. To address this problem, various coefficient recovery methods have been proposed in the past, including a divide-and-conquer approach to speed up the recovery process. However, the segmentation technique considered in the existing method operates with the assumption of a bi-modal distribution for the pixel values, but most images do not satisfy this condition. Therefore, in this work, an adaptive method was employed to perform more accurate segmentation, so that the real potential of the previous coefficient recovery methods can be unleashed. In addition, an improved rewritable adaptive data embedding method is also proposed that exploits the recoverability of coefficients. Discrete cosine transformation (DCT) patches and blocks for data hiding are judiciously selected based on the predetermined precision to control the embedding capacity and image distortion. Our results suggest that the adaptive coefficient recovery method is able to improve on the conventional method up to 27% in terms of CPU time, and it also achieved better image quality with most considered images. Furthermore, the proposed rewritable data embedding method is able to embed 20,146 bits into an image of dimensions 512×512.


2021 ◽  
Author(s):  
Joni Seppala ◽  
Honglei Zhang ◽  
Nam Le ◽  
Ramin G. Youvalari ◽  
Francesco Cricri ◽  
...  
Keyword(s):  

2021 ◽  
Author(s):  
Jukka I. Ahonen ◽  
Ramin G. Youvalari ◽  
Nam Le ◽  
Honglei Zhang ◽  
Francesco Cricri ◽  
...  
Keyword(s):  

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