Three-dimensional video compression using subband/wavelet transform with lower buffering requirements

Abstract

Three-dimensional (3-D) video compression using wavelets decomposition along the temporal axis dictates that a number of video frames must be buffered to allow for the temporal decomposition. Buffering of frames allows for the temporal correlation to be made use of, and the larger the buffer the more effective the decomposition. One problem inherent in such a set up in interactive applications such as video conferencing, is that buffering translates into a corresponding time delay. We show that 3-D coding of such image sequences can be achieved in the true sense of temporal direction decomposition but with much less buffering requirements. For a practical coder, this can be achieved by introducing an approximation to the way the transform coefficients are encoded. Applying wavelet decomposition using some types of filters may introduce edge errors, which become more prominent in short signal segments. We also present a solution to this problem for the Daubechies (1988) family of filters.

A complete non-predictive video compression scheme based on a 3D to 2D geometric transform

“…They are becoming more and more demanding in computational resources (processing and memory) when many frames have to be considered to be processed at time. Indeed, such algorithms require access to all frames in the GOF to perform the temporal transform which limit their practical implementation especially when the amount of frames in the used GOF is large (Kutil and Uhl, 1999;Khalil et al, 1999). Even when the use of large virtual memory space, the transform process may severely be affected by the requirement of making all information available not found in the main memory.…”

“…Even when the use of large virtual memory space, the transform process may severely be affected by the requirement of making all information available not found in the main memory. In other hand, if we set a maximum allowable delay, we are putting an upper limit on the number of frames that can be used for temporal decomposition resulting in lower compression efficiency (Khalil et al, 1999;Sikora, 2005). In fact, limiting the transformation to small cubes (8 × 8 × 8 blocks) also limits the potential for compression since all missed correlations between pixels and frames could be found beyond the 8-pixel surroundings.…”

Optimized Speech Compression Algorithm Based on Wavelets Techniques and its Real Time Implementation on DSP

“…During last decade, the wavelet transform has emerged as a powerful and robust mathematical tool for analyzing non-stationary signal, and has been used in wide range of signal processing applications such as: speech, image and video compression [1] [2] [3], speech denoising [4], pattern recognition [5] and electrocardiogram (ECG) signal processing [6]. Recently many new algorithms have been developed based on wavelet transform [7] [8] [9] or wavelet packets transform [10] [11] to compress speech signals.…”