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![Huffman Coding reverse [8].](publication/357427214/figure/fig3/AS:1106783531728898@1640889165999/Huffman-Coding-reverse-8.jpg)
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![Figure 3. Huffman Coding example [8].](publication/357427214/figure/fig2/AS:1106783531741184@1640889165989/Huffman-Coding-example-8_Q320.jpg)
![Figure 4. Huffman Coding reverse [8].](publication/357427214/figure/fig3/AS:1106783531728898@1640889165999/Huffman-Coding-reverse-8_Q320.jpg)
Color image compression is a good way to encode digital images by decreasing the number of bits wanted to supply the image. The main objective is to reduce storage space, reduce transportation costs and maintain good quality. In current research work, a simple effective methodology is proposed for the purpose of compressing color art digital images...
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Context 1
... proposed methodology is depicted in figure (4) as a block diagram. The stages of the suggested methodology are shown as follows: 1. Quantization: Using the traditional scalar quantization method, the image bit range is rescaled from 24 bit into 8 bits, as shown in algorithm 1. 2. S Shift Coding: Shift coding will be used to shift the residual quantized values (quantized bands, max =7, min =0). ...
Context 2
... completed code is shown on the far left of the figure (4). As seen in figure (4), the symbol a2 represents a code with only one bit, but the a2 represents a code with five bits. To put it another way, ciphersthat occur more oftenare encoded with less bits than ciphers that occur less frequently. ...
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End-to-end trainable models have reached the performance of traditional handcrafted compression techniques on videos and images. Since the parameters of these models are learned over large training sets, they are not optimal for any given image to be compressed. In this paper, we propose an instance-based fine-tuning of a subset of decoder's bias t...
Citations
... The image is the foundation for various daily applications, including sharing personal photos, transmitting news, lectures, and medical images. However, a significant issue arises due to the large file sizes associated with these images, which can impact device storage and network bandwidth (Salman and Rafea, 2020; Shihab, 2023; Mohammed et al., 2021). Image compression systems aim to address this problem by efficiently preserving image information while eliminating unnecessary redundancy found among neighboring pixels In addition, using Internet applications has become necessary, meaningful, and indispensable, as most things are currently managed via the Internet. ...
Grayscale images are extensively used due to their simplicity and cheapness in storage and transmission compared to color one of RGB base. It can also be considered a solution for color-blind people to ensure a better view of things and reading. However, unfortunately, it is still quite overburdened with redundancy(s) in which the data compression exploits them efficiently depending on the type and way of redundancy removal. This work introduces a hybrid compression system to compress grayscale images using the adaptive pixel-based technique (PBT) of optimized modeling base, with incorporated Minimize Matrix Size Algorithm (MMSA) of three digits values (C321) to encode the residual compactly along the need to overhead information (index, mean). Adapting traditional PBT led to overcoming the problems in the conventional PBT system in terms of large size. It achieved an acceptable reduction in bytes for the deterministic part (M, Indx) of over 400 bytes and the deterministic part (Res), where the size was reduced to more than 5000 bytes on average. The size has been reduced by nearly 50% compared to traditional PBT. The tested results indicate higher quality compared to the standard JPEG and traditional PBT in terms of performance. This includes a Compression Ratio (CR) of 13 and a PSNR of 48 dB.
... Steganography is a technique for hiding the existence of the message. [5], [6], [7] The words steganos (which means cover or protection) and graphy (which means to write) are combined to form the term steganography. Steganography is therefore defined as protected writing in its literal sense. ...
The need to protect multimedia data security—including audio, video, and text—and the data it contains has persisted, whether in physical or digital form. This is due to the fact that the fabrication and fake of Multimedia data is widespread throughout the world, causing serious costs to people, societies, and the industrial sectors in addition to compromising national security. People are therefore concerned about safeguarding their employment and avoiding these illegal behaviors. To safeguard sensitive data, a variety of strategies including steganography, cryptography, and coding have been used. Steganography is a suitable technique that allows the user to hide a message inside of another message (cover media). The majority of steganography research makes use of cover material, like videos, images, and sounds. Notably, because to the challenges involved in locating embedded bits in an audio recording, audio steganography is typically not prioritized. By altering the least significant bits (LSBs) of audio samples, a technique known as audio steganography (LSB) can be used to conceal information in an audio stream. The least significant bits (LSBs) in a number's binary representation are the least weighted bits and are typically undetectable to the human ear. The audio signal can have a hidden message inserted in it without significantly affecting its perceptual quality by changing or manipulating the LSBs of the audio samples. To incorporate information into an audio, However, because to the slight audio difference, an assailant or other outsider might be able to hear this. To solve this problem, it is important to alter the audio in a way that it cannot be heard by the human ear but may still be understood by other means. This document is a summary of the available research in the field. We start by outlining the fundamentals of audio steganography and how it works. Then, three categories of audio steganography—statistical and random generation; and linguistics—are described. Each class's tactics are examined, with a focus on how a special method is offered for concealing sensitive information. In addition, we survey the existing works in the advancement of strategies and algorithms relevant to audio steganography; our survey, which covers work published from 2016 to 2023, is not exhaustive. This study gathers the present approaches, difficulties, and future directions in this topic with the goal of assisting other researchers .In addition, we survey the existing works in the advancement of strategies and algorithms relevant to audio steganography.
Due to the quick development of multimedia technology, now can see the increasing amount of multimedia data that is being generated or transmitted over the internet. digital image is one form of multimedia data, that can take different formats and different sizes. And here comes the main objective of image compression in transmitting or storing the data in an effective manner by decreasing the redundant or irrelevant information without significant loss of visual quality in image data. Compression of the images is utilized in various services, including TV broadcasts, satellite imagery, martial communications, webinars, medical imaging, weather reporting, etc. This paper provides a survey of the main techniques in image compression, covering both lossy & lossless approaches. The techniques that depend on lossy compression lose some of the image details during compression, while lossless compression techniques keep the image information without losing. Vector and scalar Quantization, transform coding, block truncation Coding, etc. are all examples of lossy approaches. Run length coding, entropy encoding, statistical coding, etc. are all examples of lossless techniques. This paper will assist the researchers in learning about these techniques and choosing appropriate techniques for their work.
