# Semantic Segmentation in Learned Compressed Domain Source: [Semantic Segmentation in Learned Compressed Domain]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:31:24.160Z Authors: Jinming Liu, Heming Sun, Jiro Katto Published: 2022-09-03T07:59:34.000Z ## Abstract Most machine vision tasks (e.g., semantic segmentation) are based on images encoded and decoded by image compression algorithms (e.g., JPEG). However, these decoded images in the pixel domain introduce distortion, and they are optimized for human perception, making the performance of machine vision tasks suboptimal. In this paper, we propose a method based on the compressed domain to improve segmentation tasks. i) A dynamic and a static channel selection method are proposed to reduce the redundancy of compressed representations that are obtained by encoding. ii) Two different transform modules are explored and analyzed to help the compressed representation be transformed as the features in the segmentation network. The experimental results show that we can save up to 15.8\\% bitrates compared with a state-of-the-art compressed domain-based work while saving up to about 83.6\\% bitrates and 44.8\\% inference time compared with the pixel domain-based method. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()