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Rate-Distortion Optimized Post-Training Quantization for Learned Image Compression
Quantizing a floating-point neural network to its fixed-point representation is crucial for Learned Image Compression (LIC) because it improves decoding consistency for interoperability and reduces space-time complexity for implementation.
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