An Improved Image Compression Algorithm Using 2D DWT and PCA with Canonical Huffman Encoding

Of late, image compression has become crucial due to the rising need for faster encoding and decoding. To achieve this objective, present study proposes use of canonical Huffman coding (CHC) as an entropy coder, which entails a lower decoding time compared binary codes. For compression, discrete wavelet transform (DWT) CHC with principal component analysis (PCA) were combined. The lossy method was introduced by using PCA, followed DWT enhance efficiency. By instead PCA alone, reconstructed images have better peak signal-to-noise ratio (PSNR). In study, we also developed hybrid model combining advantages DWT, PCA. With increasing data, techniques are necessary efficient storage space. proposed technique achieved up 60% while maintaining high visual quality. This outperformed currently available in terms both PSNR (in dB) bit-per-pixel (bpp) scores. approach tested on various color images, including Peppers 512 × 3 Couple 256 3, showing improvements 17 dB 22 dB, respectively, reducing bpp 0.56 0.10, respectively. grayscale well, i.e., Lena Boat 256, showed 5 8 decrease 0.02 cases.