Dependent Scalar Quantization For Neural Network Compression

Recent approaches to compression of deep neural networks, like the emerging standard on compression of neural networks for multimedia content description and analysis (MPEG-7 part 17), apply scalar quantization and entropy coding of the quantization indexes. In this paper we present an advanced method for quantization of neural network parameters, which applies dependent scalar quantization (DQ) or trellis-coded quantization (TCQ), and an improved context modeling for the entropy coding of the quantization indexes. We show that the proposed method achieves 5.778% bitrate reduction and virtually no loss (0.37%) of network performance in average, compared to the baseline methods of the second test model (NCTM) of MPEG-7 part 17 for relevant working points.