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.
Nowadays, as communication and network technologies evolve in modern life, ensuring the confidentiality of a cryptography system has become a critical requirement. The Vigenère cipher is attracting the attention of cryptography specialists, although the Vigenère cipher algorithm has a problem. The problem is due to a repeating encryption key. As a result of the multiple cryptographic approaches described in the literature, this paper proposes a novel encryption strategy for safe and secure data exchange by utilizing a new key generation process. The proposed encryption approach avoids the issue of repeating keys. Additionally, the classic Vigenère cipher encrypts the plaintext using a 26x26 Vigenère table, the researcher modified the original Vigenère table to 95x95, which adds more potential letters, mathematical symbols, numerals, and punctuation to a standard QWERTY keyboard layout. Additionally, the researcher added case sensitivity. To observe the performance of the proposed method, the index of coincidence and entropy have been calculated. The obtained results confirm the high performance of the proposed algorithm in comparison to the other algorithms used in this paper. The primary goal of this paper is to make cryptanalysis extremely complex and to promote data security.
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