SEAOTTER: Sensor Embedded Autoencoding with One-Time Transcode for Efficient Reconstruction

Hugging Face Daily Papers Papers
compression cloud-robotics autoencoding jpeg transcoding learned-latents robotics

Summary

Introduces SEAOTTER, a compression framework for cloud robotics that combines learned latent representations with standard JPEG compatibility, achieving faster encoding/decoding and higher accuracy at extreme compression ratios.

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 .
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Source: https://huggingface.co/papers/2606.03940

Abstract

A compression framework for cloud robotics combines learned latent representations with standard JPEG compatibility to achieve faster encoding and decoding while maintaining high perceptual quality.

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. Recentasymmetric autoencodersdeliver 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 alearned latentwith the broad usability of a standard JPEG file. Since naivetranscodingdegrades performance, we propose a learnable JPEG color andquantization transformthat enables increased accuracy for global, dense, and vision-language-based perception. Using SEAOTTER, we train both general-purpose andtask-aware transcodingpipelines for a pre-trained, frozen encoder. At acompression ratioof 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 .

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