An Efficient Encoding Algorithm Using Local Path on Huffman Encoding Algorithm for Compression

Erdal, Erdal; Ergüzen, Atilla

doi:10.3390/app9040782

Cited by 13 publications

(5 citation statements)

References 27 publications

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“…The proposed method consists of two DCNNs, C-DCNN, R-DCNN, huffman encoding (HE) [16] and decoding. During compression, the input image is represented compactly to preserve salient information of the medical image.…”

Section: Proposed Image Compression Methodsmentioning

confidence: 99%

A novel compression methodology for medical images using deep learning for high-speed transmission

Navaneethakrishnan,

Shanmugam

2024

IJRES

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Medical imaging is a rapidly growing field having a high impact on the early detection, diagnosis and surgical planning of diseases. Several imaging techniques such as computed tomography (CT), magnetic resonance imaging (MRI) and ultrasound (US) imaging generate a higher volume of data, necessitating additional storage and communication requirements. Hence, image compression is utilized in medical field to reduce redundancy and alleviate memory and bandwidth issues. This paper presents a novel deep learning-based compression method to reduce the size of medical images. This method employs a deep convolutional neural network for learning compact representations of medical images, then coded by a Huffman encoder. The compression process is reversed to reconstruct the original image. Several tests are conducted to compare the results with other wellknown compression methods. The proposed model achieved a mean peak signal-to-noise ratio (PSNR) of 42.82 dB with storage space saving (SSS) of 96.15% for CT, 43.88 dB with SSS of 96.25% for MRI, 46.29 dB with SSS of 96.07% for US and 43.51 dB with SSS of 96.95% for X-ray images. The findings showed that the proposed compression technique could greatly compress the image size, saving storage space, facilitating better transmission and preserving critical diagnostic information.

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Section: Proposed Image Compression Methodsmentioning

confidence: 99%

A novel compression methodology for medical images using deep learning for high-speed transmission

Navaneethakrishnan,

Shanmugam

2024

IJRES

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“…In the extreme case, we can convert a network to a binary connect model, where all parameters are þ1orÀ1 [80]. Last but not least, some information encoding methods, such as Huffman encoding, that represents frequent clusters with fewer bits and rare clusters with more bits [81], can also be used as quantization techniques, since they are efficient encoding strategies.…”

Section: Model Compressionmentioning

confidence: 99%

Marine Robotics 4.0: Present and Future of Real-Time Detection Techniques for Underwater Objects

Er¹,

Chen²,

Zhang³

2023

Artificial Intelligence

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Underwater marine robots (UMRs), such as autonomous underwater vehicles, are promising alternatives for mankind to perform exploration tasks in the sea. These vehicles have the capability of exploring the underwater environment with onboard instruments and sensors. They are extensively used in civilian applications, scientific studies, and military missions. In recent years, the flourishing growth of deep learning has fueled tremendous theoretical breakthroughs and practical applications of computer-vision-based underwater object detection techniques. With the integration of deep-learning-based underwater object detection capability on board, the perception of underwater marine robots is expected to be enhanced greatly. Underwater object detection will play a key role in Marine Robotics 4.0, i.e., Industry 4.0 for Marine Robots. In this chapter, one of the key research challenges, i.e., real-time detection of underwater objects, which has prevented many real-world applications of object detection techniques onboard UMRs, is reviewed. In this context, state-of-the-art techniques for real-time detection of underwater objects are critically analyzed. Futuristic trends in real-time detection techniques of underwater objects are also discussed.

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“…The approach in the algorithm assigns symbols from the alphabet while changing the coded words according to their frequency. Higher frequency codes are indicated with shorter codes for a higher compression result [22].…”

Section: Huffman Encoding Algorithmmentioning

confidence: 99%

Text-based Steganography using Huffman Compression and AES Encryption Algorithm

Ali

Kadhim

2021

eijs

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In every system of security, to keep important data confidential, we need a high degree of protection. Steganography can be defined as a way of sending confidential texts through a secure medium of communications as well as protecting the information during the process of transmission. Steganography is a technology that is used to protect users' security and privacy. Communication is majorly achieved using a network through SMS, e-mail, and so on. The presented work suggested a technology of text hiding for protecting secret texts with Unicode characters. The similarities of glyphs provided invisibility and increased the hiding capacity. In conclusion, the proposed method succeeded in securing confidential data and achieving high payload capacity by using the Huffman compression algorithm, which was implemented on an unlimited text length. In addition, this approach has the ability to hide a single bit in every digit or letter in the cover file. Also, the approach meets the cognitive transparency and does not make the modifications apparent on the original data. The method suggested in this work increases the security level through coding a secret message before embedding it within the cover text, with the use of the Advanced Encryption Standard (AES) algorithm.

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An Efficient Encoding Algorithm Using Local Path on Huffman Encoding Algorithm for Compression

Abstract: Keywords: encoding algorithm; compression; Huffman encoding algorithm; arithmetic coding algorithm

Cited by 13 publications

References 27 publications

A novel compression methodology for medical images using deep learning for high-speed transmission

A novel compression methodology for medical images using deep learning for high-speed transmission

Marine Robotics 4.0: Present and Future of Real-Time Detection Techniques for Underwater Objects

Text-based Steganography using Huffman Compression and AES Encryption Algorithm

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