Detailed image structures and visual textures (of stochastic nature) in digital video are difficult to compress
efficiently. At medium to low bit rates, texture flattening and blocking artifacts appear, even when using advanced
video coding standards such as H.264/MPEG-4 AVC. In this paper, we propose video compression systems to
compress stochastic textures by exploiting rank-reduction techniques. In this work, rank reduction is implemented
by applying a singular value decomposition and selective transmission of the primary signal components as in
principal component analysis. In the low bit-rate range, our implementation shows encouraging results compared
to H.264/MPEG-4 AVC, not only in rate-distortion performance, but also in the improved visual quality of the
reconstructed videos.
Low-bitrate digital video often suffers from the artifact of texture flattening. Texture synthesis can be used to revive the
removed texture. Patch-based synthesis provides a quite general method for texture synthesis. However, this method still
requires a substantial bitrate to transmit example patches. We propose a method for stochastic texture synthesis which
requires only a very low bitrate (less than 1 kbit/sec) that can replace patch-based synthesis for random textures. Spatial
correlation is modeled as a 2-dimensional Moving Average (MA) process. To achieve a faithful representation of
temporal evolution, we use a translation+scaling motion model combined with a finite impulse response (FIR) filter.
Experiments show that we can successfully reduce texture flattening for a range of random textures such as grass and
roads.
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