We propose a fast arbitrary-ratio image resizing method for transcoding of the compressed images. The downsizing process in the discrete cosine transform (DCT) domain can be implemented by truncating high-frequency coefficients, whereas the upsizing process is implemented in the DCT domain by padding zero coefficients to the high-frequency part. The proposed method combines a fast inverse and forward DCT of composite length for arbitrary-ratio upsizing or downsizing. According to the resizing ratio, truncating the high-frequency coefficients and padding zeros are appropriately considered by combining the inverse DCT and forward DCT. The proposed method shows a good peak signal-to-noise ratio and less computational complexity compared with the spatial-domain and previous DCT-domain image resizing methods.

Image resizing of a compressed image can be performed in a spatial domain via decompression, resizing, and recompression, which is computationally inefficient. In order to reduce computation amount, efficient resizing approaches in the compressed domain have been developed. In this paper, we propose a novel approach to resize images that are compressed by 8 /spl times/ 8 block-discrete cosine transform (DCT)-based coding. The proposed image-resizing method is performed in the DCT domain and exploits the multiplication-convolution property of the DCT. Filter characteristics of the proposed image resizing method are analyzed by using a multirate filter bank representation. Since the characteristics of our resizing filters are better than those of others and the proposed image resizing method is computationally efficient, it can be widely used in a transcoder and shows visually fine results.

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 x 8 block-DCT coefficients, its resized image is also obtained in 8 x 8 block-DCT coefficients. The proposed approach is computationally fast and produces visually fine images with high PSNR.

This paper has developed a fast variable-length decoder which uses a plane separation technique to reduce the processing time of the feedback path in the decoder. The developed decoder performs two shift processes and a decision process concurrently. Therefore, the processing time in the feedback path of our developed variable length decoder can be improved and determined by the longest time among the three processes, not by the sum of their processing times together. Our simulation results show that the total processing time of our developed decoder makes about 30% improvement from that of the Sun and Lei's (1991, 1992) decoder and their modified decoder when they are implemented with field programmable logic device.

A programmable and scalable parallel architecture is proposed for the real-time encoding/decoding of HDTV images and for nonlinear editing of the compressed video data. It only uses the intra-mode compression/decompression so that nonlinear editing can be performed easily and high-quality images can be recovered. Spatially partitioned image data are concurrently processed by multiple parallel processing units (PUs). Each PU consists of a programmable parallel digital signal processor, called multimedia video processor (MVP; TMS320C80), and reconfigurable field programmable logic devices (FPLDs). The performance of the REDS is described in terms of the required MVP cycles for transform coding and the FPLDs throughput for entropy coding. Robust rate distortion-optimized quantization matrices for HDTV images are presented.