Machine-Oriented Compression# Dan Jacobellis Acknowledgments Abstract Introduction Organization Machine Perceptual Quality Background Evaluation Framework for Machine Perceptual Quality Key Findings Learned Compression for Compressed Learning Introduction Sandwiched Asymmetric Autoencoder via Wavelet Packet Transform Implementation: WPT, Entropy Bottleneck, and Entropy Coding Evaluation Lightweight, Versatile Codec Design Introduction Background and related work Encoding-Efficient Asymmetric Autoencoder Design Evaluation Conclusion and Future Work Variable-Rate Compression and Projection-Pursuit Encoding Introduction Full-Input, Residual-Output Autoencoding Evaluation Conclusion Video and Real-Time Sensing Introduction Background Related work Local Inference with Delay-Conditioned Remote Assistance Design and implementation Evaluation Bounded Performance Under Variable Delay Conclusion Sensor-Embedded Autoencoding with One-Time Transcode Introduction Negative-Distortion Transcoding via Jointly Trained JPEG Proxy Accuracy Gains from Negative-Distortion Transcoding Conclusion