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5 Machine Learning Techniques for Image Optimization
( go to the article → https://datafloq.com/read/5-machine-learning-techniques-image-optimization/11924 )
Image optimization techniques are implemented for the purpose of reducing the size of an image file. The goal is to help browsers quickly load images, and ensure user experience remains positive. Another advantage of image optimization is reducing network bandwidth consumption, which can help speed up file transfers and save on bandwidth costs. The most common way to optimize images is image compression. However, to significantly reduce file size, many are forced to use lossy compression, which often degrades the quality of the image. Machine learning (ML) and artificial intelligence (AI) algorithms are currently being developed and trained for the purpose of improving image optimization and image quality. Image Optimization Challenges There are a few key challenges facing image optimization methods and techniques: Image format limitations—image optimization algorithms operate on existing image files. Some image formats have lossy compression, which removes some of the information from the original image to reduce file size. This creates a need for “upsampling”, ways to increase image resolution and add the missing data back to the image. Resizing—a highly effective way to optimize images is to resize them to the actual size needed by the web page or application. There may be several sizes for the same image, for ...Read More on Datafloq
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