Application of Huffman Algorithm and Unary Codes for Text File Compression

Space research organizations, hospitals and military air surveillance activities, among others, produce a huge amount of data in the form of images hence a large storage space is required to record this information. In hospitals, data produced during medical examination is in the form of a sequence of images and are very much correlated; because these images have great importance, some kind of lossless image compression technique is needed. Moreover, these images are often required to be transmitted over the network. Since the availability of storage and bandwidth is limited, a compression technique is required to reduce the number of bits to store these images and take less time to transmit them over the network. For this purpose, there are many state-of the-art lossless image compression algorithms like CALIC, LOCO-I, JPEG-LS, JPEG20000; Nevertheless, these compression algorithms take only a single file to compress and cannot exploit the correlation among the sequence frames of MRI or CE images. To exploit the correlation, a new algorithm is proposed in this paper. The primary goals of the proposed compression method are to minimize the memory resource during storage of compressed data as well as minimize the bandwidth requirement during transmission of compressed data. For achieving these goals, the proposed compression method combines the single image compression technique called super spatial structure prediction with inter-frame coding to acquire grater compression ratio. An efficient compression method requires elimination of redundancy of data during compression; therefore, for elimination of redundancy of data, initially, the super spatial structure prediction algorithm is applied with the fast block matching approach and later Huffman coding is applied for reducing the number of bits required for transmitting and storing single pixel value. Also, to speed up the block-matching process during motion estimation, the proposed method compares those blocks that have identical sum and leave the others; therefore, the time taken by the block-matching process is reduced by minimizing the unnecessary overhead during the blockmatching process. Thus, in the proposed fast lossless compression method for medical image sequences, the twostage redundant data elimination process ultimately reduces the memory resource required for storing and transmission. The method is tested on the sequences of MRI and CE images and produces an improved compression rate.

SummaryThe ability to design smarter, more predictive healthcare solutions for use in the community (at work and at home) and in healthcare facilities has been greatly enhanced by recent developments in the Internet of Health Things (IoHT) and cyber physical systems (CPS). These data collected by such medical sensors will be sent in a large quantity to the fog gateway of IoHT networks. These data should be forwarded to far cloud for further analysis and processing. Therefore, sending all of these data over the IoHT network to the data center will impose a significant burden on the IoHT network. This paper proposed a new lossless electroencephalogram (EEG) compression technique (NeLECoT) for fog computing‐based IoHT networks. It encodes the data of the patient at the fog gateway prior to sending it to the data center, thereby reducing the data's size. In the fog node, the NeLECoT combines three efficient techniques: clustering based on density‐based spatial clustering of applications with noise (DBSCAN), RLE (run length encoding), and Huffman encoding (HE). The clustering based on DBSCAN separates a massive volume of captured data into small groups of closely related (or similar) data. RLE encodes clustered EEG data, and the resulting file is encoded with HE. The fog gateway then transmits the encoded file to the cloud. Numerous simulation experiments were carried out, and the findings demonstrated that the suggested NeLECoT achieved better results than the competing techniques in terms of transmitted data size, compression ratio, compression power, compression time, decompression time, and average compression power.

The article considers the relevance of the development of lossless image compression and transmission algorithms and their application for creating transport video surveillance systems. A brief overview of lossless transport image compression methods is provided. We propose a method for compressing transport plots based on the pyramid-recursive method of splitting the source image into polygons of various shapes and sizes. We consider two new algorithms for implementing the proposed method that are fundamentally different from each other: with a transition to the spectral region and without a transition to the spectral region of the original signal to ensure lossless compression. The results of testing various well-known lossless compression algorithms are analyzed: series length, Huffman, and arithmetic encoding, and compared with the proposed algorithms. It is shown that the proposed algorithms are more efficient in terms of compression ratio (2—3 times) compared to the known ones, while the computational complexity increases approximately by more than 3-4 times.

The development of telecommunications technology in the era of globalization happens very rapidly to help people to communication and one of the communication technology is using telephone device. However, the absence of security in securing data/information of delivery Short Message Service (SMS) that is confidential causes the message easily stolen by unauthorized parties. SMS sent via the BTS will be accepted Message Service Center (SMSC) been causing a crack misuse of the information. One way to secure the data from the data communication process is by cryptography. This research using descriptive statistical research methods. This research was conducted by analyzing the security by implementing the RC6 algorithm in cryptography and compression applications based on android SMS. The RC6 algorithm is an symmetric cryptographic algorithm that use the same key in encryption and decryption of SMS message. The encryption process that changes the number bits of data affects the number of SMSs that need to be sent, so text compression is required. The Huffman algorithm is a lossless data compression algorithm in which data compression does not eliminate one byte and is stored as originally. So the result of decompress is same with original ciphertext. Testing is done using Blackbox Method.This research was conducted to solve the security problem of sending SMS with cryptography and send data can be compressed to save SMS cost.Keywords : Cryptography, Encryption, Decryption, SMS, Mobile, RC6, Huffman,android

This paper provides different kinds of techniques for lossless data compression and comparison between them. By eliminating redundant bits, data compression decreases the file size. In order to reduce the capacity needed for that data, it decreases the redundant bits in data representation and thus uses the bandwidth effectively to reduce the communication cost. Compression of data saves file volume, network bandwidth and speeds up the transfer speed as well. Lossless and Lossy are the two techniques for data compression. Lossless compression maintains the data properly.

An antidictionary is particularly useful for data compression, and on-line electrocardiogram (ECG) lossless compression algorithms using antidictionaries have been proposed. They work in real-time with constant memory and give better compression ratios than traditional lossless data compression algorithms, while they only deal with ECG data on a binary alphabet. This paper proposes on-line ECG lossless compression for a given data on a finite alphabet. The proposed algorithm gives not only better compression ratios than those algorithms but also uses less computational space than they do. Moreover, the proposed algorithm work in real-time. Its effectiveness is demonstrated by simulation results.

This paper presents a promising time-domain based lossless compression technique called Log2 Sub-band encoding, which is designed for using on wireless biomedical devices. Data compression can help to save power from the wireless transceiver during data transmission, and from the storage medium during reading and writing, ultimately leading to a longer battery life of the device. The performance of Log2 Sub-band is measured in terms of its compression ratio (CR) on EEG data and its power consumption. Our simulation results indicate a CR that is comparable and even superior to the well-known Huffman coding, whilst consuming minimal hardware resource. The simulations primarily use electroencephalogram (EEG) data, and the power consumption during compressing process is given to evaluate the system's improvement on its power performance. The possible influence of different biomedical data on technique's performance will also be considered and the signal classification potential of Log2 Sub-band will be noted.

Two-dimensional barcodes called Quick Response codes (QR codes) are commonly used to store data, such as URLs, contact details, and product details. As a means of information sharing, they are growing in popularity due to their ability to store large amounts of data in a small footprint. However, the increasing demand for data storage necessitates larger QR code sizes, potentially impacting readability and scanning efficiency. This study looks into how to use lossless compression techniques like Huffman Coding and Lempel-Ziv-Welch (LZW) to make QR codes store more information without losing any of their accuracy. According to the tests, LZW had an average compression ratio of 35%, and Huffman coding had 40%. This demonstrated that both approaches could considerably compress QR code sizes. Furthermore, both methods ensured reliability by maintaining 100% data integrity after decoding. The results show that adding lossless compression to QR codes makes them work better, which means they can be used to store more data in smaller spaces. This research provides a foundation for further advancements in QR code optimisation, particularly in multi-layered and multicoloured QR code systems.

Research at the Space Dynamics Laboratory(SDL) / Utah State University in image compression has resulted in the development of a hybrid compression algorithm for space related images. The basic algorithm is a hybrid of lossy and lossless compression techniques that have been implemented in hardware [I]. The lossy encoder uses Mean Removed Vector Quantization(MRVQ). The Lossless compressor is an entropy encoder used to encode the errors introduced by the lossy MRVQ. A constant bit rate is maintained by thresholding the MRVQ errors based on the number of previously encoded bits. This paper compares Huffman and Rice Compression techniques to be used in the lossless stage of the hybrid compressor. These method have been selected because hardware to implement them already exist. A single chip implementation of the Rice algorithm is available that processes at the rates required[2].

Huffman coding is a simple lossless data compression technique that tries to take advantage of entropy by using a Variable-Length encoding to build a code table to encode a source file symbols. In this paper, we have re-visited Huffman coding and enhance it by considering the second order form of characters rather the first order form of characters. Results showed that using the second order form improves the compression ratio by around 8% than the existing Huffman coding.

The multiple array technique proposed in this paper is a lossless compression technique that involves no transformation, and achieves a very good compression ratio. This technique is based on forming three arrays to indicate and code the changes in the adjacent pixels. The arrays thus formed can be further compressed using Huffman coding. The computational complexity is greatly reduced thus producing faster compressions and decompressions. Due to the combination of simplicity and compression potential of this technique, the algorithm "enjoys the best of both worlds". This technique attains compression ratios similar to or superior than the lossless image compression standards presently available. Due to the high compression ratio, the average bits required to represent a pixel is greatly reduced. Thus, this algorithm is optimally suited for telemedicine having fast transmission rates.

<p>The need of data compression at smart Edge/Fog-based gateways is undeniable as data compression can significantly reduce the amount of data that has to be transmitted over a network. This, in turn, has a direct impact on reducing transmission latency and increasing network bandwidth. In time-critical and data-sensitive IoT applications such as healthcare, lossless data compression is preferable as compressed data can be recovered without losing any information. However, it is not an easy task to choose a proper lossless data compression algorithm for IoT applications as each lossless data compression method has its own advantages and disadvantages. This paper focuses on the analysis of lossless data compression algorithms run at smart Edge/Fog gateways. Widely used lossless data compression is run at different hardware which is often used as smart Fog/Edge gateways. The latency of data compression and the compression rate in different cases of input data sizes are analyzed. The paper provides guidelines for choosing a proper lossless data compression algorithm for time-critical IoT applications. <br /></p>

WITHDRAWN: Implementing lossless compression during image processing by integrated approach

Summary form only given. The next-generation space telescope (NGST) will produce about 600 GB/day, assuming we use the NASA yardstick 8k/spl times/8k NIR camera (16 bits/pixel), save and transmit 64 non-destructive read-outs per image, and the camera is in continuous use (about 80 observations/day, 10/sup 3/ s each). However, with an L2 halo orbit, the NASA NGST study estimates a downlink rate of 5.35 GB/day using X-band. Clearly the volume of data to downlink must be reduced by at least a factor of 100. Astronomical images are noisy. This fact makes them difficult to compress by lossless compression algorithms such as Huffman, Lempel-Ziv, run-length, or arithmetic code. However, they also have the virtue of showing similar values among adjacent pixels. Techniques such as Rice's algorithm (Rice et al., 1993) and derivatives (White and Becker, 1998; Stiavelli and White, 1997) can take advantage of this. We present the way in which some of these compression techniques would work with NGST images. Unfortunately, these lossless algorithms give us compression ratios that still exceed the telemetry guidelines. We have also looked into the feasibility of doing lossy compression by scaling the original image prior to the lossless compression. Under this scheme, we find substantial data reduction with a negligible effect on the data quality.

While lossy compression techniques are used for images, sound. where feature reproduction is more important than the actual data, it is necessary to use lossless compression for certain files containing data, programmes. Lossless compression techniques are not as effective as lossy ones and usually require a lot of computation for both compression and decompression. We presented a novel lossless compression techniques based on linear feedback shift registers. The advantage of this technique is that decompression is fairly simple and can be implemented without extensive computations. The decompression algorithm is also useful for implementation in hardware. In this paper we presented the details of the LFSR based algorithm. The results on various types of data such as images, sound files and text files presented and the results compared against existing lossless compression techniques.