Efficient scalar quantization of exponential and Laplacian random variables

By Gary J. Sullivan. 1996

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Jiuxiang Gu
2. Jianfei Cai
3. Amir Shahroudy
4. Jun Liu
5. Jason Kuen
6. G. Wang
7. Zhenhua Wang
8. Bing Shuai

Citations in the corpus

• Recent advances in convolutional neural networks (2015, methodology): “These powerful models require more data for training in order to avoid overfitting.”
• DRED: Deep REDundancy Coding of Speech Using a Rate-Distortion-Optimized Variational Autoencoder (2022, methodology): “An efficient way of quantizing a Laplace-distributed variable [22] is to use a fixed quantization step size, except around zero, where all values of q e ∈ ] θ, θ [ quantize to zero, with θ > arising from rate-distortion optimization.”
• Autoencoder Based Image Compression: Can the Learning be Quantization Independent? (2018, unknown): “For transformed coefficients having a zero-mean Laplace distribution, [18] proves that a uniform reconstruction quantizer (URQ) with constant decision offsets approaches the optimal scalar quantizer in terms of squared-error distortion for any quantization step size.”