| Abstract |
The object of this dissertation is to investigate rate-distortion optimization
and to evaluate the prospects of adaptive vector quantization for digital video
compression.
Rate-distortion optimization aims to improve compression performance using
discrete optimization algorithms. We rst describe and classify algorithms
that have been developed in the literature to date. One algorithms is extended
in order to make it generally applicable; the correctness of this new procedure
is proven. Moreover, we compare the complexity of the aforesaid algorithms,
rst implementation-independent and then by run-time experiments. Finally,
we propose a technique to speed up one of the aforementioned algorithms.
Adaptive vector quantization enables adaption to sources with unknown or
non-stationary statistics. This feature is important for digital video data since
the statistics of two subsequent frames is usually similar, but in the long run
the general statistics of frames may change even if scene changes are neglected.
We examine combinations of adaptive vector quantization with various stateof-
the-art video compression techniques. First we present an adaptive vector
quantization based codec that is able to encode and decode in real-time using
current PC technology. This codec is rate-distortion optimized and adaptive
vector quantization is applied in the wavelet transform domain. The organization
of the wavelet coecients is then made more ecient using adaptive
partition techniques. Moreover, the main adaptability mechanism of adaptive
vector quantization, the so-called codebook update, is studied. Finally,
a combination of adaptive vector quantization and motion compensation is
taken into consideration. We show that for very low bitrates adaptive vector
quantization performs on prediction residual frames better or at least as well
as discrete cosine transform coding. |