SEAOTTER: Sensor Embedded Autoencoding with One-Time Transcode for Efficient Reconstruction
2026-06-02 • Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionMachine LearningRobotics
AI summaryⓘ
The authors focus on improving how robots send and use images without needing too much computing power or bandwidth. They created a system called SEAOTTER that compresses images efficiently by combining smart data encoding with the widely used JPEG format. Their method makes encoding and decoding faster and keeps image quality good for tasks like recognizing objects, while still working with existing JPEG tools. Compared to newer codecs, SEAOTTER is faster and even improves accuracy on a common image classification test.
roboticsimage compressionautoencoderJPEGlatent representationbandwidthencodingdecodingrate-distortion trade-offImageNet
Authors
Dan Jacobellis, Neeraja J. Yadwadkar
Abstract
In robotics systems, vast amounts of visual data are easily captured at high resolution using low-cost, low-power hardware. Yet, limited bandwidth and on-device compute resources prevent full utilization when transmitted via conventional codecs like JPEG/MPEG. Newer codecs, like AV1/AVIF, improve the rate-distortion trade-off, but demand far more resources for encoding, impractical without custom ASICs. Recent asymmetric autoencoders deliver high quality under extreme power and bandwidth constraints, but add prohibitive decoding cost and use bespoke formats that ignore decades of infrastructure built around standards like JPEG. To address these limitations, we introduce a compression framework for cloud robotics based on a Sensor Embedded Autoencoder paired with a One-Time Transcode for Efficient Reconstruction (SEAOTTER). Because the sensor, cloud, and consumer stages face very different power and bandwidth budgets, SEAOTTER combines the compactness of a learned latent with the broad usability of a standard JPEG file. Since naive transcoding degrades performance, we propose a learnable JPEG color and quantization transform that enables increased accuracy for global, dense, and vision-language-based perception. Using SEAOTTER, we train both general-purpose and task-aware transcoding pipelines for a pre-trained, frozen encoder. At a compression ratio of 200:1 and compared to AVIF, we observe 7 times faster encoding, 3.5 times faster decoding, and +8% ImageNet top-1 accuracy, while retaining compatibility with JPEG infrastructure. Our code is available at https://github.com/UT-SysML/seaotter .