Fast intra prediction in the transform domain

In this paper, we present a fast intra prediction method based on separating the transformed coefficients. The prediction block can be obtained from the transformed and quantized neighboring block generating minimum distortion for each DC and AC coefficients independently. Two prediction methods are proposed, one is full block search prediction (FBSP) and the other is edge based distance prediction (EBDP), that find the best matched transformed coefficients on additional neighboring blocks. Experimental results show that the use of transform coefficients greatly enhances the efficiency of intra prediction whilst keeping complexity low compared to H.264/AVC

Transcoding of MPEG Bitstreams

This paper discusses the problem of transcoding as it may occur in, for instance, the following situation. Suppose a satellite transmits an MPEG-compressed video signal at say 9 Mbit/s. This signal must be relayed at a cable head end. However, since the cable capacity is only limited, the cable head end will want to relay this incoming signal at a lower bit-rate of, say, 5 Mbit/s. The problem is how to convert a compressed video signal of a given bit-rate into a compressed video signal of a lower bit-rate. The specific transcoding problem discussed in this paper is referred to as bit-rate conversion. Basically, a transcoder used for such a purpose will consist of a cascaded decoder and encoder. It is shown in the paper that the complexity of this combination can be significantly reduced. The paper also investigates the loss of picture quality that may be expected when a transcoder is in the transmission chain. The loss of quality as compared to that resulting in the case of transmission without a transcoder is studied by means of computations using simplified models of the transmission chains and by means of using computer simulations of the complete transmission chain. It will be shown that the presence of two quantizers, i.e. cascaded quantization, in the transmission chain is the main cause of extra losses, and it will be shown that the losses in terms of SNR will be some 0.5 ¿ 1.0 dB greater than in the case of a transmission chain without a transcoder

Human Motion Capture Data Tailored Transform Coding

Human motion capture (mocap) is a widely used technique for digitalizing human movements. With growing usage, compressing mocap data has received increasing attention, since compact data size enables efficient storage and transmission. Our analysis shows that mocap data have some unique characteristics that distinguish themselves from images and videos. Therefore, directly borrowing image or video compression techniques, such as discrete cosine transform, does not work well. In this paper, we propose a novel mocap-tailored transform coding algorithm that takes advantage of these features. Our algorithm segments the input mocap sequences into clips, which are represented in 2D matrices. Then it computes a set of data-dependent orthogonal bases to transform the matrices to frequency domain, in which the transform coefficients have significantly less dependency. Finally, the compression is obtained by entropy coding of the quantized coefficients and the bases. Our method has low computational cost and can be easily extended to compress mocap databases. It also requires neither training nor complicated parameter setting. Experimental results demonstrate that the proposed scheme significantly outperforms state-of-the-art algorithms in terms of compression performance and speed