Efficient Schemes for Compressed-Domain Image Resizing

An efficient method for DCT-domain image resizing with mixed field/frame-mode macroblocks

This paper deals with discrete cosine transform (DCT)-domain image resizing as compressed domain processing. Previous DCT-domain image resizing methods assume that all macroblocks are in the frame mode. In this paper, we propose an efficient method for DCT-domain image resizing with mixed field/frame-mode macroblocks. This method is developed from the investigation of the DCT-domain image resiz...

L/M-fold image resizing in block-DCT domain using symmetric convolution

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 ...

A Fast Algorithm for Resizing Images in the DCT Domain

Abstract For many applications such as image transmission over channels with variable bandwidths and image display on devices at different resolution levels, image resizing is widely demanding. Recently, image resizing algorithms in the compressed domain have drawn a lot of attention to computation efficiency. In this paper, an efficient image resizing algorithm based on discrete cosine transfo...

Semantic-Based Image Retrial in the VQ Compressed Domain using Image Annotation Statistical Models

Efficient image down-conversion for mixed field/frame-mode macroblocks

An efficient image resizing algorithm in the compressed domain with mixed field/frame-mode macroblocks is proposed. A 16 16 field/frame-mode macroblock is converted into an 8 8 reduced block in the discrete cosine transform (DCT) domain using a modified inverse DCT (IDCT) kernel which performs stronger lowpass filtering than the simple truncation in the DCT domain. Experimental results show tha...