Title : Characterization and Modeling of Image Sensor Helps to Achieve Lossless Image Compression
link : Characterization and Modeling of Image Sensor Helps to Achieve Lossless Image Compression
Characterization and Modeling of Image Sensor Helps to Achieve Lossless Image Compression
EETimes-Europe: Swiss startup Dotphoton claims to achieve 10x lossless image compression:
"Dotphoton’s Jetraw software starts before the image is created and uses the information of the image sensor’s noise performance to efficiently compress the image data. The roots of the image compression date back to the research questions of quantum physics. For example, whether effects such as quantum entanglement can be made visible for the human eye.
Bruno Sanguinetti, CTO and co-founder of Dotphoton, explained, “Experimental setups with CCD/CMOS sensors for the quantification of the entropy and the relation between signal and noise showed that even with excellent sensors, the largest part of the entropy consists of noise. With a 16-bit sensor, we typically detected 9-bit entropy, which could be referred back solely to noise, and only 1 bit that came from the signal. It is a finding from our observations that good sensors virtually ‘zoom’ into the noise.”
Dotphoton showed that, with their compression method, image files are not affected by loss of information even with compression by a factor of ten. In concrete terms, Dotphoton uses information about the sensor’s own temporal and spatial noise."
The company's Dropbox comparison document dated by January 2020 benchmarks its DPCV algorithm vs other approaches:
— per-pixel calibration and linearization. Even for high-end cameras, each pixel may have a different efficiency, offset and noise structure. Our advanced calibration method perfectly captures this information, which then allows both to correct sensor defects and to better evaluate whether an observed feature arises from signal or from noise.
— quantitatively-accurate amplitude noise reduction. Many de-noising techniques produce visually stunning results but affect the quantitative properties of an image. Our noise reduction methods, on the other hand, are targeted at scientific applications, where the quantitative properties of an image are important and where producing no artefacts is critical.
— color noise reduction using amplitude data and spectral calibration data
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