• Kiran Basavannappagowda
• Jaswanth RS

In numerous applications, the transmission and storage of images are frequently required. The smaller the image, the lower the associated costs of transmission and storage. Therefore, data compression techniques are often employed to minimize the storage space utilized by the image. One such technique involves applying Singular Value Decomposition (SVD) to the image matrix. In this process, a digital image is subjected to SVD, which decomposes the image into three matrices. Singular values are then utilized to reconstruct the image, resulting in a representation with a reduced set of values, thereby decreasing the required storage space. The objective is to compress the image while retaining the essential features that characterize the original image. SVD is versatile and can be adapted to any arbitrary matrix of m x n size, whether square, reversible, or non-reversible. The compressed image generated by the modified SVD is then used as input for a 2D Discrete Wavelet Transform (DWT) image compression algorithm to achieve a superior compression ratio with the same Mean Square Error (MSE). The performance metrics used are the compression ratio, MSE, Peak Signal-to-Noise Ratio, Bitrate and Structural Similarity Index (SSIM).

Singular Value Decomposition (SVD); 2D discrete wavelet transform (2D DWT); Inverse Discrete wavelet (IDWT); lossy image compression; Image Performance Metrics; Python; Linear Algebra

"An Improved Image Compression Algorithm Using SVD And 2D-DWT", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.f110-f116, May-2024, Available :http://www.jetir.org/papers/JETIR2405511.pdf

"An Improved Image Compression Algorithm Using SVD And 2D-DWT", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. ppf110-f116, May-2024, Available at : http://www.jetir.org/papers/JETIR2405511.pdf