Image Compression and Technology

Image Compression and Technology

In the age of high-resolution digital cameras, smartphones and tablets, image compression is an essential tool for preserving image data and optimizing storage and transmission. It allows images to be stored on the device for longer, reducing battery consumption and environmental impact. It also saves bandwidth for data transmission, which speeds up content delivery and reduces network congestion and cost. It is particularly important in areas with limited or expensive bandwidth, such as autonomous cars or remote sensing satellite imagery. In the medical field, compression enables rapid transfer of magnetic resonance imaging (MRI), ultrasound and computed tomography (CT) images for diagnosis.

Image Jpegxl.Io compression involves encoding or converting an image file into one that consumes less space, without compromising the quality of the original file. A number of methods are available to achieve this, including lossy and lossless compression. Lossy image compression algorithms reduce the amount of information in an image by removing redundant pixels, e.g. the JPEG image format uses a technique called transform coding to average out color in small blocks of an image using a discrete cosine transformation. Run-length encoding is another example of lossless image compression that encodes repeated pixels by recording the number of times they occur, rather than recording each individual pixel.

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ROI-based image compression methods are designed to compress only the parts of an image that are of interest, preserving their quality and requiring less storage or bandwidth than the full image. The performance of these techniques is typically measured by the peak signal-to-noise ratio (PSNR) metric. In this article, we will explore a variety of ROI-based techniques and categorize them based on the PSNR and other metrics that measure image quality.