A Lightweight Contextual Arithmetic Coder for On-Board Remote Sensing Data Compression

The Consultative Committee for Space Data Systems (CCSDS) has issued several data compression standards devised to reduce the amount of data transmitted from satellites to ground stations. This paper introduces a contextual arithmetic encoder for on-board data compression. The proposed arithmetic encoder checks the causal adjacent neighbors, at most, to form the context and uses only bitwise operations to estimate the related probabilities. As a result, the encoder consumes few computational resources, making it suitable for on-board operation. Our coding approach is based on the prediction and mapping stages of CCSDS-123 lossless compression standard, an optional quantizer stage to yield lossless or near-lossless compression and our proposed arithmetic encoder. For both lossless and near-lossless compression, the achieved coding performance is superior to that of CCSDS-123, M-CALIC, and JPEG-LS. Taking into account only the entropy encoders, fixed-length codeword is slightly better than MQ and interleaved entropy coding.

I. Introduction

Remote sensing imagery is becoming an invaluable tool for governments, rescue teams, and aid organizations to manage infrastructure and natural resources, to appraise climate changes, or to give support when natural disasters strike. Since remote sensing images tend to be very large, high-performance compression techniques are of paramount importance.