Edge-preserving image compression using adaptive lifting wavelet transform

Abstract

In this paper, a novel 2-D adaptive lifting wavelet transform is presented. The proposed algorithm is designed to further reduce the high-frequency energy of wavelet transform, improve the image compression efficiency and preserve the edge or texture of original images more effectively. In this paper, a new optional direction set, covering the surrounding integer pixels and sub-pixels, is designed. Hence, our algorithm adapts far better to the image orientation features in local image blocks. To obtain the computationally efficient and coding performance, the complete processes of 2-D adaptive lifting wavelet transform is introduced and implemented. Compared with the traditional lifting-based wavelet transform, the adaptive directional lifting and the direction-adaptive discrete wavelet transform, the new structure reduces the highfrequency wavelet coefficients more effectively, and the texture structures of the reconstructed images are more refined and clear than that of the other methods. The peak signal-to-noise ratio and the subjective quality of the reconstructed images are significantly improved.

Optimal deep learning based image compression technique for data transmission on industrial Internet of things applications

“…A compressive sensing technique was presented in the literature 9 by exploiting the interscale and intrascale dependencies using directional lifting wavelet transform. In the literature, 10 an adaptive lifting wavelet transform was presented that adjusts the lifting way to local image blocks that is hold extra edge details of the images. An investigation about the features of LIDAR data was made and effective representation techniques were also developed to remote sensing images.…”

Invertible update-then-predict integer lifting wavelet for lossless image compression

“…The second-generation wavelets based on lifting scheme have achieved substantial recognition [4][5][6], which are used in the fields of signal analysis [7], image coding [8][9][10][11], palmprint identification [12], moving object detection [13], especially since their integration in the JPEG2000 standard [14][15][16][17][18]. The lifting scheme is an efficient and powerful tool to compute the wavelet transform.…”