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Physics experiments produce enormous amount of raw data, counted in petabytesper day. Hence, there is large effort to reduce this amount, mainly by usingsome filters. The situation can be improved by additionally applying some datacompression techniques: removing redundancy and optimally encoding the actualinformation. Preferably, both filtering and data compression should fit in FPGAalready used for data acquisition - reducing requirements of both data storageand networking architecture.  We will briefly explain and discuss some basic techniques, for a better focusapplied to design a dedicated data compression system basing on a sample datafrom a prototype of a tracking detector: 10000 events for 48 channels. We willfocus on the time data here, which after neglecting the headers and applyingdata filtering, requires on average 1170 bits/event using the current coding.Encoding relative times (differences) and grouping data by channels, reducesthis number to 798 bits/channel, still using fixed length coding: a fixednumber of bits used for a given value. Using variable length Huffman coding toencode numbers of digital pulses for a channel and the most significant bits ofvalues (simple binning) reduces further this number to 552 bits/event. Usingadaptive binning: denser for frequent values, and an accurate entropy coder weget further down to 455 bits/event - this option can easily fit unusedresources of FPGA currently used for data acquisition. Finally, using separateprobability distributions for different channels, what could be done by asoftware compressor, leads to 437bits/event, what is 2.67 times less than theoriginal 1170 bits/event.

Designing dedicated data compression for physics experiments within FPGA already used for data acquisition