the data compression book

Yes. The titles discuss lossless compression, which preserves all original information, and lossy compression, which reduces data by discarding less important details to save bits.

Page counts vary across this selection, ranging from about 92 pages up to 608 pages, with several titles falling between those extremes.

They are aimed at advanced readers—graduate students and professional practitioners—covering material from algorithmic foundations to practical standards and applications.

Some titles include practical elements such as projects, solved problems (often with Python), tutorial exercises, and an explanatory glossary to support learning.

They focus on image and video coding, compression techniques for data mining and big-data workflows, holographic and 3D imaging compression, and related areas in computer vision and pattern recognition.

They describe methods such as Huffman coding, scalar and vector quantization, transforms and subband coding, wavelet-based scalable compression, and the use of neural networks and deep learning for feature selection and dimensionality reduction.

They highlight a growing need for hologram-compression techniques to enable efficient storage and transmission, and provide overviews of current methods for compressing and representing digital holographic data for both still and moving sequences.