Navigation: LaesieWorks Image Compression Homepage

This site part is about the near-lossless compression of images and movies (compression without visible loss of image quality).
The lossless search for repetitions, and the lossy search for things that are less important. What I find even more interesting than extreme reduction of file size, is the search for the essence of images.

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A one hour movie
A digital movie is a-lot-of-data.
How many movies are there in this movie space?
Can the library address be smaller than the movie itself?

Bits and Combinations
What a bit is, counting bits and the power of combining bits

Compression basics
The search for repetitions and less important details.

What a camera can capture
A camera can only capture an image of reality, not reality itself, making the image quality lossy by definition. But camera's with RAW can capture the most information.

What the eye can sense
How many colors can your monitor show, and how many colors and details can your eyes distinguish?

Noise is no image
We should exlude noise!

The illusion of tones,
and the reduction of combinations.

Less Combinations
Eight bits have 256 combinations, but if you know that eight out of eight bits are zero, than there's just one combination possible: 00000000.

The Shortest Codes
If the goal is small files, then use the shortest codes.

Camera Motions
A hand held videocamera its digital images are all different, even without noise, because even the slightest change of camera position results in a different image on the image sensor.



Panasonic Lumix GH2 noise
What is its best ISO setting?

Optical Compact Disc Code
How code is stored on a CD
(the 120 mm Compact Disc - Digital Audio, Red Book CD from 1982, by Philips & Sony).

MegaPixels & Image sensor size
Why most digital compact cameras produce such incredible low quality, and why large cameras have better pixels.

C60 Buckyballs
How C60 Buckyballs could store 250000 times more data, relative to an audio CD.

DigiCom Shirts
One design so far: "Lossless Compression of Random Data"

Giesbert Nijhuis