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This paper describes a novel approach to digital image compression using a new mathematical transform: the curvelet transform. The transform has shown promising results over wavelet transform for 2D signals. Wavelets, though well suited to point singularities have limitations with orientation selectivity, and therefore, do not represent two-dimensional singularities (e.g. smooth curves) effectively. This paper employs the curvelet transform for image compression, exhibiting good approximation properties for smooth 2D functions. Curvelet improves wavelet by incorporating a directional component. The curvelet transform finds a direct discrete-space construction and is therefore computationally efficient. In this paper, we divided 2D spectrum into fine slices using iterated tree structured filter bank. Different amount of quantized curvelet coefficients were then selected for lossy compression and entropy encoding. A …