Diagnostically lossless video compression for angiogram data using a wavelet-based texture modelling approach

This paper presents a lossy, wavelet-based approach for the compression of digital angiogram video. An analysis of the high-frequency sub-bands of a wavelet decomposition of an angiogram image reveals significantly sized regions containing no diagnostically important information. The encoding of the high-frequency sub-band wavelet coefficients in such regions proves to be burdensome, although if removed, the coefficients are notable by their absence. This paper aims to model these wavelet coefficients using a texture modeling approach. This is only performed in regions which are considered diagnostically unimportant, with diagnostically important regions encoded as normal. The effect of this procedure significantly reduces the bit-rate of diagnostically unimportant areas of the image without a perceptible loss of image quality. The effectiveness of the algorithm at different bit-rates is assessed by a consultant cardiologist with the key aim of identifying any degradation in the diagnostic content of the images.