Multiscale SSIM (MS-SSIM) is an advanced top-down interpretation of how the human visual system interprets images. MSSIM was extended to three-component SSIM (3-SSIM) by applying non-uniform weights to the SSIM map over three different region types: edges, texture, and smooth areas. The mean SSIM (MSSIM) is a real-valued quality index that aggregates SSIM by averaging over all image pixels. The structural similarity (SSIM) index alleviates this by measuring, pixel-wise, how similar two images are by considering quality as perceived by humans. While simple to compute and having clear physical meanings, they tend not to match perceived visual quality. Widely used choices include the mean squared error (MSE) and peak signal-to-noise ratio (PSNR). Given an image I and its compressed version I ˜, a quality metric q ( I, I ˜ ) ∈ R + measures how perceptually close I ˜ is to I. Our method, called Compressing Dense Medial Descriptors (CDMD), achieves higher-compression ratios at similar quality to the well-known JPEG technique and, thereby, shows that skeletons can be an interesting option for lossy image encoding. We use this benchmark to derive optimal parameters for dense skeletons. Secondly, we propose a benchmark to assess the encoding power of dense skeletons for a wide set of natural and synthetic color and grayscale images. First, we improve the encoding power of dense skeletons by effective layer selection heuristics, a refined skeleton pixel-chain encoding, and a postprocessing compression scheme. In this paper, we fill this gap with two main contributions. Yet, their encoding power, measured by the quality and size of the encoded image, and how these metrics depend on selected encoding parameters, has not been formally evaluated. Recently, dense skeletons have been proposed as an extension of classical skeletons as a dual encoding for 2D grayscale and color images. your pictures are *not* automatically uploaded unless you explicitly request it.Skeletons are well-known descriptors used for analysis and processing of 2D binary images. in-app HD option is for *all* the panoramas you take on *all* your devices - with the same AppleID, we only stitch the pictures you take with the app, our app shoots only cylindrical panoramas,
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