Layered image resizing in compression domain
Image Communication
A novel approach for fast codebook re-quantization
Pattern Recognition
An Adaptive Image Resizing Algorithm in DCT Domain
IEICE - Transactions on Information and Systems
Pre- and post-shift filtering for blocking removing in downsizing transcoding
IEEE Transactions on Circuits and Systems for Video Technology
Adaptive up-sampling method using DCT for spatial scalability of scalable video coding
IEEE Transactions on Circuits and Systems for Video Technology
Down-sampling design in DCT domain with arbitrary ratio for image/video transcoding
IEEE Transactions on Image Processing
A fast arbitrary-ratio downscaling algorithm for video transcoding
SIP '07 Proceedings of the Ninth IASTED International Conference on Signal and Image Processing
On the use of color appearance modeling for efficient compressed-domain image enhancement
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Algebraic signal processing theory: sampling for infinite and finite 1-D space
IEEE Transactions on Signal Processing
Image filtering in the compressed domain
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
| Hi-index | 0.02 |
Image resizing is to change an image size by upsampling or downsampling of a digital image. Most still images and video frames on digital media are given in a compressed domain. Image resizing of a compressed image can be performed in the spatial domain via decompression and recompression. In general, resizing of a compressed image in a compressed domain is much faster than that in the spatial domain. We propose a novel approach to resize images with L/M resizing ratio in the discrete cosine transform (DCT) domain, which exploits the multiplication-convolution property of DCT (multiplication in the spatial domain corresponds to symmetric convolution in the DCT domain). When an image is given in terms of its 8×8 block-DCT coefficients, its resized image is also obtained in 8×8 block-DCT coefficients. The proposed approach is computationally fast and produces visually fine images with high PSNR.