אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב
יום שלישי, 09.07.2013, 13:30
The digital video is a 3D spatio-temporal signal formed as a sequence of 2D
images (frames) captured over time. Hence, a sampled video represents a
large amount of information. As a result, video transmission and storage
systems require efficient coding and should be analyzed from a
rate-distortion perspective. Specifically, good quality video coding for low
bit-rate applications has great importance for transmission over
narrow-bandwidth channels and for storage with limited memory capacity.
Improvement of low bit-rate video compression by temporal-scaling appears in
the literature in the form of frame-skipping mechanisms. However, no
theoretic explanation was given for the complete compression and
frame-skipping system. Moreover, frame-skipping methods usually require
modifications in the original codec.
Previous studies showed, theoretically and experimentally, the benefits of
spatial scaling for coding of image and video signals at low bit-rates.
In this work, we expand a previous analysis for image compression and adapt
it to video signals. Down-scaling in the spatial and temporal dimensions is
examined. We show, both analytically and experimentally, that at low
bit-rates, we benefit from applying spatio-temporal scaling. The proposed
method includes down-scaling before the compression and a corresponding
up-scaling afterwards, while the codec itself is left unmodified.
We propose analytic models for low bit-rate compression and frame-rate up
conversion (FRUC). Specifically, we theoretically model the
motion-compensated prediction of an available and absent frames as in coding
and frame-rate up-conversion (FRUC) applications, respectively. The model is
designed for multi-resolution analysis.
In addition, we formulate a bit-allocation procedure and propose a method
for finding the optimal down-scaling factors of a given video by its
second-order statistics and a bit-budget.
We validate our model with experimental results of H.264 compression.
M.Sc. research under the supervision of prof. Alfred M. Bruckstein