8 accepted papers
Adaptive prediction is important in the compression of non-stationary signals, and a common remedy is to switch between appropriately designed prediction modes. This paper presents a near optimal procedure to design prediction modes for an adaptive compression system. The main challenges include: in…
Scalable coding is potentially useful in content distribution over unreliable channels, as it enables meaningful reconstruction when the hierarchical bitstream is only partially received. However, its deployment in conjunction with predictive coding may result in considerable performance degradation…
This paper considers near optimal design of predictive compression system that accounts for packet loss over unreliable networks. Major challenges to address include, propagation of errors due to packet loss through the prediction loop, mismatch between statistics used for design and during operatio…
Conventional pixel-domain block matching temporal (inter) prediction is suboptimal, since it ignores the underlying spatial correlation. Hence in our recent research we proposed transform domain temporal prediction (TDTP), wherein spatially decorrelated transform coefficients are individually predic…
Prediction is used in virtually all compression systems. When such a compressed signal is transmitted over unreliable networks, packet losses can lead to significant error propagation through the prediction loop. Despite this, the conventional design technique completely ignores the effect of packet…
While several real world signals, such as speech, image and sensor network data, are modeled as hidden Markov sources (HMS) for recognition and analysis applications, their typical compression exploits temporal correlations by modeling them as simple (non-hidden) Markov sources. However, the inheren…
Today's diverse data consumption devices and heterogeneous network conditions require content to be coded at different quality levels. Conventional scalable coding, which generates hierarchical layers that refine quality incrementally, introduces a performance penalty, as most sources are not succes…
Traditional temporal prediction relies on motion compensated pixel copying operations. Such per-pixel temporal prediction was shown in prior work to be sub-optimal since it ignores the underlying spatial correlation. Transform domain temporal prediction (TDTP) was thus proposed previously to first a…