Image Compression with SVD: A Mathematical Approach

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The project delves into image compression through a Linear Algebra lens, comparing Singular Value Decomposition (SVD) to traditional JPEG methods. Team members explore the mathematical theory and practical application of SVD, focusing on matrix conversions, SVD decomposition, and quality analysis using peak signal-to-noise ratio (PSNR). Methodology involves Python libraries for preprocessing and energy analysis across different ranks. Results show rapid singular value decay and varied...