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Summary form only given. All pipelines are subject to corrosion and require inspection in accordance with regulatory requirements to ensure human safety. Intelligent pipeline inspection gauges (PIGs) have provided reliable online inspection of pipelines, supplying operators with detailed information about pipeline condition. We present a method for the diagnostically lossless compression of pipeline inspection data and discuss important pipeline features, e.g. welds, cracks and erosion objects. The dataset, transverse field inspection (TFI) data, is a new type of pipeline inspection data in contrast to the traditional magnetic flux leakage (MFL) inspection data. The nature of the data makes feature preservation essential. TFI pipeline features have been collected, classified and analysed and examples are shown. Feature detection is desirable in order to identify regions of diagnostic interest. Incorporation of region-of-interest (ROI) into the SPIHT encoding scheme enables the allocation of a greater proportion of the total allowance of bits to the regions of the image identified as diagnostically significant. Our quality assessment is based on the preservation of important defect parameters to ensure diagnostically lossless performance. We present results comparing performance between ROI SPIHT and non-ROI SPIHT.