8,900 research outputs found
Multiple description video coding for stereoscopic 3D
In this paper, we propose an MDC schemes for stereoscopic 3D video. In the literature, MDC has previously been applied in 2D video but not so much in 3D video. The proposed algorithm enhances the error resilience of the 3D video using the combination of even and odd frame based MDC while retaining good temporal prediction efficiency for video over error-prone networks. Improvements are made to the original even and odd frame MDC scheme by adding a controllable amount of side information to improve frame interpolation at the decoder. The side information is also sent according to the video sequence motion for further improvement. The performance of the proposed algorithms is evaluated in error free and error prone environments especially for wireless channels. Simulation results show improved performance using the proposed MDC at high error rates compared to the single description coding (SDC) and the original even and odd frame MDC
A Universal Parallel Two-Pass MDL Context Tree Compression Algorithm
Computing problems that handle large amounts of data necessitate the use of
lossless data compression for efficient storage and transmission. We present a
novel lossless universal data compression algorithm that uses parallel
computational units to increase the throughput. The length- input sequence
is partitioned into blocks. Processing each block independently of the
other blocks can accelerate the computation by a factor of , but degrades
the compression quality. Instead, our approach is to first estimate the minimum
description length (MDL) context tree source underlying the entire input, and
then encode each of the blocks in parallel based on the MDL source. With
this two-pass approach, the compression loss incurred by using more parallel
units is insignificant. Our algorithm is work-efficient, i.e., its
computational complexity is . Its redundancy is approximately
bits above Rissanen's lower bound on universal compression
performance, with respect to any context tree source whose maximal depth is at
most . We improve the compression by using different quantizers for
states of the context tree based on the number of symbols corresponding to
those states. Numerical results from a prototype implementation suggest that
our algorithm offers a better trade-off between compression and throughput than
competing universal data compression algorithms.Comment: Accepted to Journal of Selected Topics in Signal Processing special
issue on Signal Processing for Big Data (expected publication date June
2015). 10 pages double column, 6 figures, and 2 tables. arXiv admin note:
substantial text overlap with arXiv:1405.6322. Version: Mar 2015: Corrected a
typ
- âŚ