Thorough performance analysis of Haar, Cosine, Hartley and Slant transforms for Grayscale Image Colorization using Thepade's Transform Error Vector Rotation Algorithms in YCbCr and RGB color spaces

Abstract

Performance Comparison of Hartley, Cosine, Slant and Haar Transforms for Grayscale Image Colorization Using Thepade's Transform Error Vector Rotation(TTEVR), Algorithms of Vector Quantization in RGB and YCbCr Color Spaces is done here for color pallet(codebook) size 32, 64, 128, 256 and 512. Here a color pallet is generated from source(color) from which color traits need to be taken using vector quantization using Thepade's transform error vector rotation algorithms in the first stage., Then colors are, transferred to a target (grayscale) image using generated color pallet in the second stage. As there are no objective criteria for qualitative analysis of performance of the colorization quality of proposed TTEVR, here the grayscale version of original color image is recolored using proposed technique and the mean squared error between original color image and recolored image is used as quality comparison criteria. Using 15 different images for five different color pallet sizes in RGB and YCbCr color spaces experimentation is done. The proposed technique performs better in RGB color space. The obtained results show that the proposed technique using TTEVR with Haar for color pallet size 512 in RGB color space gives best performance.