In today’s digital era, the demand for digital medical images is rapidly increasing. Hospitals are transitioning to filmless imaging systems, emphasizing the need for efficient storage and seamless transmission of medical images. To meet these requirements, medical image compression becomes essential. However, medical image compression typically necessitates lossless compression techniques to preserve the diagnostic quality and integrity of the images. There are several challenges associated with medical image compression and management. Firstly, medical image management and image data mining involve organizing and accessing large volumes of medical images efficiently for clinical and research purposes. Secondly, bioimaging, which encompasses various imaging modalities like microscopy and molecular imaging, presents specific requirements and challenges for compression algorithms. Thirdly, virtual reality technologies are increasingly utilized in medical visualizations, demanding efficient compression methods to handle the high resolution and immersive nature of VR medical imaging data. Lastly, neuro imaging deals with complex brain imaging data, requiring specialized compression techniques tailored to the unique characteristics of these images. As the amount of medical image data continues to grow, image processing and visualization algorithms have to be adapted to handle the increased workload. Researchers and developers have been working on various compression algorithms to address these challenges and optimize medical image compression. This review paper compares different compression algorithms that would provide valuable insights into the strengths, limitations, and performance metrics of various techniques. It would assist researchers, clinicians, and imaging professionals in selecting the most suitable compression algorithm for their specific needs, considering factors such as compression ratio, computational complexity, and image quality preservation. By comprehensively comparing compression algorithms, this review paper contributes to advancing the field of medical image compression, facilitating efficient image storage, transmission, and analysis in healthcare settings.
Recently, the study of audio compression has become more prominent. Today's apps make use of advancements in audio signal processing, including advanced audio coding (AAC), perceptual audio coding techniques (MP3 encoding), internet radio, and other lossless audio coding systems. In this essay, we provide a summary and contrast the algorithms Huffman and Arithmetic. The method is used with a comparison of the two algorithms, Huffman and Arithmetic, on raw WAV files. The fundamental objective of the steganography technique is to increase the security of the transmitted data. Unauthorized users are unable to access or misuse the steganographic file. Audio steganography is applicable to non-technical areas as well in order to protect the privacy and confidentiality of the data. This paper also includes a review of recent work on audio steganography. For database applications like storage and transfer, audio compression is essential. This essay discusses how compression techniques are applied to audio using various transform coding and focuses on the advantages of transform coding in compared to current methods. By using the transform coding technique, numerous attempts have been made to completely eradicate or minimize audio noise, and the study has produced a number of fruitful results. Examining current fractal coding techniques for digital multimedia compression is the aim of the fractional compression study. It discusses strategies for reducing encoding time, which is considered to be the primary challenge in fractal compression, as well as suggested fractal coding techniques in the audio and image domains. In order to use the most widely used communication platforms, large audio file sizes must be transferred via digital audio; this poses substantial challenges for storage and preservation. This article makes good use of audio.It is advised to use a mixed transform coding scheme as a compressive method. The audio file size was greatly decreased while maintaining high quality, and the compression results are promising. The cascaded prediction approach was improved and reported in Audio Compression utilizing OLS+ and CDCCR Method. Comparing three primary predictor block types with varying levels of complexity included two complicated prediction tech- niques with backward adaptation, namely extended Active Level Classification. Extended Ordinary Least Square (OLS+) and the Extended ALCM+ Model
The thesis describes of character recognition process of various Tamil scripts using various classifier and the work proposed noise image and segmentation process for the individual characters image of letters from each other. After the process, an important feature extraction step is used to recognize the Ancient characters accurately with hypothesis combination. Feature describes the characteristic of object uniquely for Variability method of complexity image. Noise Reduction Process with Filtering Method is one of the feature extraction, which is the process of extracting information from test data which is most relevant for classification purpose relevant the type of error with blur image of letter or characters. The technique extracts the basic components of Tamil Characters and then it can be translates into the components for additional recognition measures to the probability of blur image. Finally a recognition process system is proposed for the characters in Tamil script. The data set characters are sampled from the script automatically or tools based. The proposed Hypothesis classifier is tested on quite number samples images of letter of ancient Tamil images.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.