
pmid: 18003298
This paper presents a context adaptive coding method for image sequences in hemodynamics. The proposed method implements motion compensation through of a two-stage context adaptive linear predictor. It is robust to the local intensity changes and the noise that often degrades these image sequences, and provides lossless and near-lossless quality. Our preliminary experiments with lossless compression of 12 bits/pixel studies indicate that, potentially, our approach can perform 3.8%, 2% and 1.6% better than JPEG-2000, JPEG-LS and the method proposed in [1], respectively. The performance tends to improve for near-lossless compression.
Angiography, Video Recording, Reproducibility of Results, Signal Processing, Computer-Assisted, Data Compression, Image Enhancement, Sensitivity and Specificity, Image Interpretation, Computer-Assisted, Humans, Algorithms
Angiography, Video Recording, Reproducibility of Results, Signal Processing, Computer-Assisted, Data Compression, Image Enhancement, Sensitivity and Specificity, Image Interpretation, Computer-Assisted, Humans, Algorithms
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 1 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
