Increasing the Huffman generation code algorithm to equalize compression ratio and time in lossless 16-bit data archiving

Hidayat, Tonny; Zakaria, Mohd Hafiz; Pee, Ahmad Naim Che

doi:10.1007/s11042-022-14130-1

Cited by 8 publications

(3 citation statements)

References 61 publications

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“…The idea is that the mistake should manifest as low-magnitude noise given the assumption that repeating material, such as audio, exists. The CR and PSNR are the metrics used to measure the efficiency of the compression procedure in this set of tests [28]. The need to use alternative audio measurement of PEAQ is mentioned in [29].…”

Section: Modified Lossy Mlzw Audio Compressionmentioning

confidence: 99%

Effective Audio Compression using Enhanced Encoding Method

2024

IJSER

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The rapid growth of big data and the emergence of new technologies have posed significant challenges in data compression, particularly in the domain of audio compression. Audio compression has gained widespread applications in advanced audio coding, MP3 encoding, web radio, and lossless audio coding techniques. In this paper, a newly proposed technique is present aimed at improving audio compression. The proposed technique consists of two main stages: preprocessing and the application of compression techniques. The preprocessing stage involves several steps. Firstly, the raw audio data is subjected to set normalization to ensure consistent scaling. Next, the data is divided into smaller segments to facilitate the application of Discrete Cosine Transform (DCT) and quantization on each segment. The quantization parameter is determined based on the bit per sample of the audio test file and the number of channels. To implement the Modified MLZW compression technique, the data is converted into byte values, and a word reference is constructed based on these bytes. To evaluate the performance of the proposed technique, multiple audio files have been utilized in experimental tests. The results demonstrate that the proposed technique achieves smaller MSE of 2.05*10 -10 and significant improvements in terms of compression efficiency with reduction in processing time to 200 ms. The proposed methods works better and ensure that the audio quality is good and the system can be used for transmitting audio over different media that depend on lossy audio compression.

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Section: Modified Lossy Mlzw Audio Compressionmentioning

confidence: 99%

Effective Audio Compression using Enhanced Encoding Method

2024

IJSER

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“…Convolutional Neural Networks have similarities with the traditional ANN method, where there are neurons that will perform self-optimization through the learning process. The most striking difference between CNNs and ANNs is that CNNs are mainly used in the field of pattern recognition on an image [38], [39].…”

Section: B Convolutional Neural Networkmentioning

confidence: 99%

Grouping of Image Patterns Using Inceptionv3 For Face Shape Classification

Hidayat,

Astuti,

Yaqin

et al. 2023

JOIV : Int. J. Inform. Visualization

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The human face is an extraordinary part where nearly everybody is not quite the same as each other. One perspective that should be visible plainly is the shape. Face shape grouping can be used for amusement, security, or excellence. One technique that can be utilized in picture grouping is the InceptionV3 model. InceptionV3 is the structure of the Convolutional Neural Network (CNN) created by Google, which can tackle picture examination and item discovery issues. This engineering is utilized to order face shapes into five classes: Round, Heart, Square, Oblong, and Oval. At that point, the Google Pictures dataset goes through the pre-handling stage, and the Shrewd Edge Identifier is applied to each picture. Hair turns into a commotion. Consider recognizing the side of the face because it does not make any difference what the hairdo resembles. What is important is the side of the face. When there is a dataset of elongated class and heart class with a comparable hairdo, InceptionV3 will identify the component and expect the two pieces of information to come from a similar class. The exchange learning strategy is done in preparation for the last Layer of ImageNet's InceptionV3 model. This strategy puts the high precision level with an exactness of 93% preparation and testing between 88% - 98%. InceptionV3 could arrange upwards of 692 from 747 datasets or around 92.65%. The most reduced information class is the heart class, where out of 150 information, InceptionV3 can characterize upwards of 130 information.

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“…In conjunction with, for instance, the LZSS method (used in gzip, PKZip, ARJ, and LHArc), BWT-based algorithms, JPEG (Wallace, 1991), and MPEG compression, Run-Length Encoding and Move-To-Front coding (Skibinski, 2006), Huffman compression is utilized. According to trial analysis and results of another research based on manual mathematical and statistical calculations, 4-ary/MQ delivers high compression results with a very quick procedure, therefore using it to reduce data on local storage media, hosting/cloud, and bandwidth has numerous advantages (Hidayat et al, 2022). In another research, a compression method combines the information-rearranging Lempel-Ziv-Welch (LZW) algorithm and the Huffman encoding method, both of which are lossless compression techniques.…”

Section: Literature Surveymentioning

confidence: 99%

Quaternary Tree Structure as a Novel Method for Huffman Coding Tree

Habib,

Islam,

Rahman

2023

Journal of Computer Science

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The binary code used by the current Huffman-based techniques slows down decoding speed. The quaternary Huffman coding method is a fresh take on the traditional Huffman coding that is suggested in this study. Each symbol is encoded into a quaternary code rather than a binary code using the quaternary Huffman coding technique. For Huffman coding, a quaternary code stream necessitates a shallower Huffman tree. Less traverse time could be a benefit of a shorter Huffman tree, with the enhancement of both encoding and decoding speed. The main objectives of this research are to verify the feasibility of using quaternary code in terms of both decoding speed and memory usage mathematically, to develop a new code generation technique called quaternary Huffman coding, and to analyze the performance of the proposed system in terms of both storage and decoding time. In this study, we first quantitatively investigate the quaternary tree structure's features for building Huffman codes. We thoroughly investigate the new tree structure's nomenclature and substantiate the findings. It is found in the research that the quaternary code performs better than binary code in terms of decoding speed in data compression and decompression techniques.

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Increasing the Huffman generation code algorithm to equalize compression ratio and time in lossless 16-bit data archiving

Cited by 8 publications

References 61 publications

Effective Audio Compression using Enhanced Encoding Method

Effective Audio Compression using Enhanced Encoding Method

Grouping of Image Patterns Using Inceptionv3 For Face Shape Classification

Quaternary Tree Structure as a Novel Method for Huffman Coding Tree

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