A Fast and Efficient Approach for Image Compression Using Curvelet Transform

In this paper a novel image compression technique using features of wavelet and curvelet transforms is proposed to improve efficiency and compression performance. Indeed, the curvelet transform is one of the recently developed multiscale transforms which is especially designed to represent efficiently curves and edges in an image. In the proposed method, the compression algorithm involves the Haar wavelet transform to decompose the image into four frequency sub-bands. The lowest frequency sub-band coefficients are processed using Set Partitioning In Hierarchical Trees (SPIHT) encoding. Meanwhile, Fast Discrete Curvelet Transform (FDCT) is applied to the remaining frequency sub-bands. The FDCT output coefficients are then quantized according to the sub-band they belong to. The lowest frequency FDCT output coefficients are quantized using Differential Pulse Code Modulation, the medium frequency coefficients are processed using SPIHT, whereas the high frequency coefficients are removed. Experimental results demonstrate that our method provides high performance for edge detection compared to existing techniques particularly for images with abrupt changes. In addition, this new image coding and decoding approach is powerful in terms of computation time. Moreover, the proposed method reveals significant improvement in compression ratio and decoded peak-signal-to-noise-ratio.