Bits are allocated to various subbands to minimize a particular cost function to achieve compression in subband coding. The most common cost function is the L2 norm based on mean squared error (MSE). However, the MSE often fails to correspond to the perceptual quality of the image, especially at low bit rate. In this paper, we allocate bits into various subbands by minimizing the Minkowsky metric -- a commonly used perceptual distortion measure. We then design the quantizer for each subband independent of each other based on the allocated bits. Experimental results indicate improved perceptual quality for the compressed images using Minkowsky metric compared to that of using the MSE metric.
We described an adaptive denoising method to improve image quality in a wavelet-based image compression process that uses dithered quantization. In our method, the second-order moment of the quantization noise is made independent of the signal by random quantization. Then, the quantization noise is reduced by thresholding wavelet coefficients. We first obtained a fixed threshold using any known technique. Then, a neighborhood is searched for the optimal threshold to optimize some cost function.
We applied dithered quantization to image compression using a wavelet transform, scalar quantization method. The results indicate that dithered quantization could change the noise characteristics of the reconstructed image.
In lossy image compression schemes, often some distortion measure is minimized to arrive at a desired target bit rate. The distortion measure that has been most studied is the mean-squared-error (MSE). However, perceptual quality often does not agree with the notion of minimization of mean square error1 . Since MSE can not guarantee the optimality of perceptual quality, others error measures have been investigated. Others have found strong mathematical and practical perspective to choose a different error measure other than MSE, especially for image compression2. In Ref. 2 it is argued that the mean absolute error (MAE) measure is a better error measure than MSE for image compression from a perceptual standpoint. In addition, the MSE measure fails when only a small proportion of extreme observations is present3. In this paper we develop a bit allocation algorithm to minimize the MAE rather than MSE