Recognition-Aware Learned Image Compression

Abstract

Learned image compression methods generally optimize a rate-distortion loss, trading off improvements in visual distortion for added bitrate. Increasingly, however, compressed imagery is used as an input to deep learning networks for various tasks such as classification, object detection, and superresolution. We propose a recognition-aware learned compression method, which optimizes a rate-distortion loss alongside a task-specific loss, jointly learning compression and recognition networks. We augment a hierarchical autoencoder-based compression network with an EfficientNet recognition model and use two hyperparameters to trade off between distortion, bitrate, and recognition performance. We characterize the classification accuracy of our proposed method as a function of bitrate and find that for low bitrates our method achieves as much as 26% higher recognition accuracy at equivalent bitrates compared to traditional methods such as Better Portable Graphics (BPG).

arXiv commentElectronic Imaging Symposium, Computational Imaging XX Conference, January 2022

References in corpus (11)

• EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
• Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation
• Variational image compression with a scale hyperprior
• Joint Autoregressive and Hierarchical Priors for Learned Image Compression
• Density Modeling of Images using a Generalized Normalization Transformation
• Real-Time Adaptive Image Compression
• Fixing the train-test resolution discrepancy: FixEfficientNet
• A Unified End-to-End Framework for Efficient Deep Image Compression
• Learned Image Compression with Discretized Gaussian Mixture Likelihoods and Attention Modules
• An End-to-End Joint Learning Scheme of Image Compression and Quality Enhancement with Improved Entropy Minimization
• Generalized Octave Convolutions for Learned Multi-Frequency Image Compression