Enhancing image coders by using spatial noise shaping (SNS)

We have developed and demonstrated that spatial noise shaping (SNS) can enhance the performance of an image coder. SNS runs open-loop 2-D linear prediction (LP) in the frequency domain instead of in the time domain as compared to the standard LP used in speech and image coding. This predictive analysis/synthesis process over frequency possesses two important properties which result in a decoded image with superior quality compared to the one without using SNS. The first property is that since SNS can preserve the spatial structure of the image in the filter, those desirable details and sharp edges won´t be damaged by the image coder and will be restored to the decoded image at the synthesis stage of SNS. The other unique property of SNS is that it effectively adapts the spatial structure of the quantization noise to that of the image roughness. Therefore, a more efficient use of masking effects can be exploited. In this paper we first explain how to use the noise shaping capability of SNS to hide the quantization noise in visually insensitive areas, such as textures and edges, but not in flat areas. Then, the techniques used to stabilize the 2-D LP filter are discussed. Finally, we show that SNS is applicable to either spectral domain coders or time domain coders, such as wavelets