Adaptive data mapping of wavelet compressed images in MIMO systems

The adaptive data mapping of wavelet compressed images in multiple input multiple output (MIMO) wireless communication systems is proposed and analyzed. Image compression methods based on wavelet transform allow image decomposition in blocks, representing signal frequency bands at different resolutions. Blocks representing low frequencies are very important for visual image quality, while the highest frequency blocks, even lost, have almost no impact on image quality. MIMO systems offer parallel subchannels with different quality. Mapping more important data to higher quality subchannels improves the image quality. The results obtained by analog devices wavelet algorithm, implemented in a single chip ADV601, used in the MIMO system with four transmit and four receive antennas, show that the adaptive data mapping approach improves the peak signal to noise ratio (PSNR) up to 8 dB in comparison to standard data mapping.