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In this paper, we present a nonlinear image representation scheme based on a statistically-derived divisive normalization model of the information processing in the visual cortex. The input image is first decomposed into a set of subbands at multiple scales and orientations using the Daubechies (9, 7) floating point filter bank. This is followed by a nonlinear “divisive normalization” stage, in which each linear coefficient is squared and then divided by a value computed from a small set of neighboring coefficients in space, orientation and scale. This neighborhood is chosen to allow this nonlinear operation to be efficiently inverted. The parameters of the normalization operation are optimized in order to maximize the statistical independence of the normalized responses for natural images. Divisive normalization not only can be used to describe the nonlinear response properties of neurons in visual cortex, but also yields image descriptors more independent and relevant from a perceptual point of view. The resulting multiscale nonlinear image representation permits an efficient coding of natural images and can be easily implemented in a lossy JPEG 2000 codec. In fact, the nonlinear image representation implements in an automatic way a more general version of the point-wise extended masking approach proposed as an extension for visual optimisation in JPEG 2000 Part 2. Compression results show that the nonlinear image representation yields a better ratedistortion performance than the wavelet transform alone.

NONLINEAR PRIMARY CORTICAL IMAGE REPRESENTATION FOR JPEG 2000 - Applying natural image statistics and visual perception to image compression