Abstract
For centuries, human artists have been scaling images by hand, sketching with varying levels of detail depending on the demands. Image scaling is a fundamental image operation and exists for various devices including mobile phones and computers. It has great commercial values and has attracted great efforts in research. In this article, we suggest a stretch-shrink based framework for image scaling which imitates the work of a human artist. This can be done by firstly representing the image with line segments (sketches) and then stretching or shrinking the length of the representing segments under proper ratios to achieve the desired scaling. Through extensive experiments, this framework exhibits the prominent ability to achieve high quality scaled images efficiently with less storage consumption.
Highlights
Image scaling refers to representing and resizing an original image with the use of a higher or lower number of pixels
Different from the most popular interpolation based methods on image scaling nowadays, we study how to use the line-segmentation and piecewise linear approximation (PLA) techniques for image scaling which is yet unknown in current literature
As depicted in Figure 1(III), Linearization and OptimalPLR are called by NaiveScale and PLAscale respectively for image scaling
Summary
Image scaling refers to representing and resizing an original image with the use of a higher or lower number of pixels. The pixels in an image are linearized and represented by straight line segments either precisely or under maximum-error bound (L∞ norm) on a small error tolerance (for example, δ = 0.3) This guaranteed quality on errors would subsequently pass to the up-scaled image without requiring any manual adjustments such as interpolation, denoising and deinterlacing. Along with the high efficiency in both execution and quality, the proposed methods revealed that scaling operations can be realized directly from compressed data (i.e., segments), which can be significantly smaller than the total number of original data points and can be more efficiently processed This indicates that data compression technique can efficiently support data analysis (operations) in addition to the data storage reduction and data transmission acceleration.
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