Assessing fish assemblages in intertidal habitats such as oyster reefs is challenging due to the structural complexity of these systems. Underwater Visual Census (UVC) and baited remote underwater videos (BRUVs) are commonly used to characterize fish communities, however, less intrusive methodologies such as Remote Underwater Video (RUV) may be more appropriate in these habitats to capture cryptobenthic fish, or where the broad attraction caused by bait plumes poses an issue. Processing RUV footage is time consuming and there is a need to optimise this procedure to increase the efficiency of research. This study identified the optimal method for assessing fish assemblages on intertidal oyster reefs by subsampling RUV footage. We quantified how processing effort affects accuracy and precision of three different fish metrics; MaxN, MeanCount, and species richness. Results suggest that MaxN and species richness should be recorded in real time, whereas optimal sampling for MeanCount is every 60 seconds. Random and systematic methods of subsampling the three metrics were compared, with systematic proving to be the most accurate and precise choice. This study provides valuable accuracy and methodology recommendations for using RUV fish assessment in a variety of shallow intertidal habitats.