2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · 2022 · 19 citations · 32 references
ComputingComputer EngineeringComputer ScienceComputer GraphicsComputer VisionMachine LearningData CompressionGraphics Processing Unit ArchitectureGraphics Processing Unit ComputingGraphics Processing UnitsImage CompressionLossless Data CompressionCompressionCompression RatioDeep Learning AcceleratorsDeep Learning-based Image CompressionImage Processing On GpusLightweight DesignLossless Image CompressionLow-complexity Compression Algorithms
Generative model based image lossless compression algorithms have seen a great success in improving compression ratio. However, the throughput for most of them is less than 1 MB/s even with the most advanced AI accelerated chips, preventing them from most real-world applications, which often require 100 MB/s. In this paper, we propose PILC, an end-to-end image lossless compression framework that achieves 200 MB/s for both compression and decom-pression with a single NVIDIA Tesla V100 GPU, 10× faster than the most efficient one before. To obtain this result, we first develop an AI codec that combines auto-regressive model and VQ-VAE which performs well in lightweight setting, then we design a low complexity entropy coder that works well with our codec. Experiments show that our framework compresses better than PNG by a margin of 30% in multiple datasets. We believe this is an important step to bring AI compression forward to commercial use.
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A Mathematical Theory of Communication
Claude E. Shannon · Bell System Technical Journal · 1948 · 80.7K citations