APIC: Adaptive perceptual image coding based on subband decomposition with locally adaptive perceptual weighting

The perceptual subband image coder (PIC), originally introduced by R.J. Safranek and J.D. Johnston, selects a noise target level for each subband based on an empirically derived perceptual masking measure. These noise target levels are used to set the quantization level in the DPCM quantizer for every particular subband. It achieves high quality output at bit rates from 0.1 to 0.9 bits/pixel (bpp) depending on the complexity of the image. In this paper, we present an algorithm that locally adapts the quantizer step size at each pixel according to an estimate of the masking measure. This estimate is based on the already coded pixels and predictions of the not yet coded pixels. Compared to the PIC, the proposed method does not require any additional side information. In fact, it eliminates the need to transmit the quantizer step size for each subband. For comparable perceptual quality, the proposed method achieves compression gains up to 40 percent. Typical values are in the order of 20 to 30 percent, depending on the nature of the image. Our algorithm has also better performance for supra-threshold image compression since the perceptual error is distributed more evenly and is not concentrated in the most sensitive regions.