Data compression lecture notes

It reports times and compression ratios for 100s of compression algorithms over many databases. It also gives a score based on a weighted average of runtime

Does correlation improve compression?

Correlation improves compression.
This is a recurring theme in all of the approaches we have seen; the more effectively a technique is able to exploit correlations in the data, the more effectively it will be able to compress that data.
This principle is most evident in MPEG encoding.
MPEG compresses video streams.

How does model based compression work?

(e.g., the fact that faces have two eyes and one nose).
The more information is shared in the model, the less need be transmitted with any given data set.
Like wavelet compression, model-based compression works by characterizing data in terms of a deeper underlying generator.

What will be covered in a pedagogical data compression course?

Both theoretical and practical aspects of compression will be covered.
A major component of the course is learning through doing - the students will work on a pedagogical data compression library and implement specific compression techniques.

Why is data compression important?

Data compression, the field dedicated to representing information succinctly, is playing an increasingly critical role in enabling this growth.

Lossy color image compression algorithm

Color Cell Compression is a lossy image compression algorithm developed by Campbell et al., in 1986, which can be considered an early forerunner of modern texture compression algorithms, such as S3 Texture Compression and Adaptive Scalable Texture Compression.
It is closely related to Block Truncation Coding, another lossy image compression algorithm, which predates Color Cell Compression, in that it uses the dominant luminance of a block of pixels to partition said pixels into two representative colors.
The primary difference between Block Truncation Coding and Color Cell Compression is that the former was designed to compress grayscale images and the latter was designed to compress color images.
Also, Block Truncation Coding requires that the standard deviation of the colors of pixels in a block be computed in order to compress an image, whereas Color Cell Compression does not use the standard deviation.
Both algorithms, though, can compress an image down to effectively 2 bits per pixel.