You don’t need to have a complete high-quality picture in order to gain insights. Photograph by Anna Shvets, Pexels. This article presents the combination of lossy compression and lossless image compression to create a hybrid approach that uses Region Of Interest. It provides high compression and allows for efficient storage and transmission while not affecting accuracy. We live in an age where information is more valuable than any other.
For future insight, it has become a critical task to store large amounts of data. We need to have a lot of storage space and high data transfer speeds. Many sectors deal with large numbers of image data, including those in the Healthcare and Multimedia industries.
Image Compression is an essential tool for reducing image data size, while requiring less storage space and ensuring low latency.
Image Compression: Both lossy and non-lossy
Image Compression is a method of compressing data in order to reduce the size of images and lower storage costs.
There are two kinds of Image Compression. Lossy Compression can be used when not all data is necessary. Some information may be lost due to loss in compression. The image can be decoded to show the original uncompressed image, but it also shows the lost information. JPEG, WebP are two examples of lossy compression.
The lossless compression technique: It is useful in situations where information is important and cannot be lost. The image can be decoded to match the original without losing any data. Gif, PNG are two examples of lossless compression.
Introduction to ROI in Images
An image consists of three parts which are : Region of interest (ROI) Non-Region of interest (Non-ROI)
Brain MRI images can be taken of the brain. This is the area that has been affected by cancer. The region of interest is represented as the red portion. This is the only area that will be needed to allow for further examination by the doctor. The Non-ROI section shown is not of much importance and can be completely ignored.
A technique that considers the background and non-important images can be developed. To maximize compression, non-ROI (or background) is taken into account.
The hybrid approach to image compression is a combination of lossy compression and lossless compression, based on Region of Interest. ROI refers to the area that is most critical in an image. It covers only a small portion of the entire image.
The non ROI part of the image is also available. This allows the user to seperate the critical parts from the entire image. The background is also included, but it’s not the most important part. The ROI must be reduced using Lossless compression. Non-ROI may be compressed with Lossy compression. However, the background is not affected.
There are many Lossless and Lossy compression methods. After separating the input image into ROI and Non ROI images, different Lossless Compression techniques and Lossy Compression techniques will then be used. This shows Context tree weighting Lossless (CTW), for the ROI portion, and Fractal lossy compress for the Non-ROI. You can also use other lossy or lossless compression techniques instead of CTW.
Fractal Compression — Lossy Compression
Fractal compress is lossy digital compression that is primarily based upon fractals. This method works best for natural and textures images. It relies on the fact that different parts of an image can look similar. These parts are converted into mathematical data, called “fractal code” and used to create the encoded image.
Context Tree Weighting — Lossless Compression (CTW)
Context Tree Weighting, a combination of lossless compression and prediction algorithms, is one example. This provides both practical and theoretical guarantees of performance. CTW is a “group technique” that combines the expectations of many hidden variable request Markov model models. Each such model uses zero-request probability estimators.
Translation, etc. The input image is processed. Segmentation is based upon Region Of Interest. 1. Part ROI: Context tree compression is used. 2. Fractal compression is used for non-ROI parts. After merging the images, storage or transmission takes place. – The decompression process is carried out. Combining these lossy compression techniques with lossless compression is more efficient than many other methods (Huffman, Arithmetic Coding, Scalable RBC and IWT, for example). When their performance parameters have been compared.
One of these performance parameters is: The Mean Squared Error, (MSE),: This widely-used measurement criterion can be used to evaluate image quality. It measures the error between uncompressed and compressed images. It is said that the algorithm with the lowest MSE value will be the most effective.
The Compression Ratio is the ratio of the number of pixels uncompressed image (input), to a compressed image (output). It is beneficial to store and transmit images with a higher compression ratio. Peak Signal-to-Noise Ratio (PSNR), is an indicator of the difference in peak errors between compressed and uncompressed images.
This is the measure of image quality. For better quality, the PSNR value must be higher. This hybrid lossy-lossy compression method gives you higher CR, greater PSNR and lower MSE than other state-of the-art methods. Image compression is both important for research and implementation.
The hybrid technique is superior to the lossy/lossless method because it greatly increases compression rate and doesn’t affect future evaluations or insights. You can also combine lossy compression with lossless methods to increase compression without having to compromise the quality of your image.
