Different hardware implementations of designed automatic 2D to 3D video color conversion employing 2D video sequence are presented. The analyzed framework includes together processing of neighboring frames using the following blocks: CIELa*b* color space conversion, wavelet transform, edge detection using HF wavelet sub-bands (HF, LH and HH), color segmentation via k-means on a*b* color plane, up-sampling, disparity map (DM) estimation, adaptive postfiltering, and finally, the anaglyph 3D scene generation. During edge detection, the Donoho threshold is computed, then each sub-band is binarized according to a threshold chosen and finally the thresholding image is formed. DM estimation is performed in the following matter: in left stereo image (or frame), a window with varying sizes is used according to the information obtained from binarized sub-band image, distinguishing different texture areas into LL sub-band image. The stereo matching is performed between two (left and right) LL sub-band images using processing with different window sizes. Upsampling procedure is employed in order to obtain the enhanced DM. Adaptive post-processing procedure is based on median filter and k-means segmentation in a*b* color plane. The SSIM and QBP criteria are applied in order to compare the performance of the proposed framework against other disparity map computation techniques. The designed technique has been implemented on DSP TMS320DM648, Matlab’s Simulink module over a PC with Windows 7 and using graphic card (NVIDIA Quadro K2000) demonstrating that the proposed approach can be applied in real-time processing mode.