4 research outputs found
Parallel Lossless Image Compression Using Huffman and Arithmetic Coding
We show that high-resolution images can be encoded and decoded e ciently in parallel. We
present an algorithm based on the hierarchical MLP method, used either with Hu man coding
or with a new variant of arithmetic coding called quasi-arithmetic coding. The coding step can
be parallelized, even though the codes for di erent pixels are of di erent lengths; parallelization
of the prediction and error modeling components is straightforward
A novel approach for the hardware implementation of a PPMC statistical data compressor
This thesis aims to understand how to design high-performance compression
algorithms suitable for hardware implementation and to provide hardware support for
an efficient compression algorithm.
Lossless data compression techniques have been developed to exploit the available
bandwidth of applications in data communications and computer systems by reducing
the amount of data they transmit or store. As the amount of data to handle is ever
increasing, traditional methods for compressing data become· insufficient. To
overcome this problem, more powerful methods have been developed. Among those
are the so-called statistical data compression methods that compress data based on
their statistics. However, their high complexity and space requirements have prevented
their hardware implementation and the full exploitation of their potential benefits.
This thesis looks into the feasibility of the hardware implementation of one of these
statistical data compression methods by exploring the potential for reorganising and
restructuring the method for hardware implementation and investigating ways of
achieving efficient and effective designs to achieve an efficient and cost-effective
algorithm. [Continues.
Gbit/second lossless data compression hardware
This thesis investigates how to improve the performance of lossless data compression hardware
as a tool to reduce the cost per bit stored in a computer system or transmitted over a
communication network.
Lossless data compression allows the exact reconstruction of the original data after
decompression. Its deployment in some high-bandwidth applications has been hampered due to
performance limitations in the compressing hardware that needs to match the performance of the
original system to avoid becoming a bottleneck. Advancing the area of lossless data compression
hardware, hence, offers a valid motivation with the potential of doubling the performance of the
system that incorporates it with minimum investment.
This work starts by presenting an analysis of current compression methods with the objective of
identifying the factors that limit performance and also the factors that increase it. [Continues.