Lossless and lossy compression of water-column profile data from multibeam echosounders based on image predictions and multiple-context entropy-encoding

Multibeam Echosounders (MBES) are hydrographic tools used primarily to survey the seafloor bathymetry and backscatter. Modern MBES systems are not limited to the seafloor, as they can also map water column profiles, which holds important biological, thermal and chemical information of oceans and shores. Unfortunately, this feature is normally disregarded during routine surveys operations, as it generates a very large amount of data, requiring data compression for possible use in future analysis. For the compression, we propose to map the water column data into images and to compress each of them using image compressors. We devised two methods: a lossless coder based on linear predictors, and a lossy coder based on thresholding followed by lossless coding. Both methods seem to better suit the echosounder image data than traditional image coders. We tested our methods in sequences that capture different water column activities in the Bay of Brest, France. Results indicate our method outperforms other standard image compression methods, ranging from 4 to 70% average gains in compression ratio in lossless coding, and equivalent results in lossy coding. Compression-induced distortion was measured as traditional mean squared error and as analysis-parameter estimation errors.