Abstract
Approximate computing techniques have been widely used to design fault-tolerant image processing applications. This chapter presents two novel energy-efficient approximate techniques; an image compression technique, and an image denoising technique. The image compression algorithm modifies the conventional DCT compression scheme to achieve around 70% reduction in required adders (and therefore energy) while maintaining the quality of compressed images. On the other hand, the image denoising technique introduces an additional step length parameter to the conventional unconstrained total variation-based inexact Newton scheme. The proposed scheme retains quality and requires fewer iterations making it a faster and more energy-efficient alternative.
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Huang, J., Almrib, H.A.F., Nandha Kumar, T., Lombardi, F. (2022). Approximate Computing in Image Compression and Denoising. In: Liu, W., Lombardi, F. (eds) Approximate Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-98347-5_21
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