Learned layered coding for Successive Refinement in the Wyner-Ziv Problem

• URL: http://arxiv.org/abs/2311.03061v1
• Date: Mon, 6 Nov 2023 12:45:32 GMT
• Title: Learned layered coding for Successive Refinement in the Wyner-Ziv Problem
• Authors: Boris Joukovsky and Brent De Weerdt and Nikos Deligiannis
• Abstract summary: We propose a data-driven approach to explicitly learn the progressive encoding of a continuous source. This setup refers to the successive refinement of the Wyner-Ziv coding problem. We demonstrate that RNNs can explicitly retrieve layered binning solutions akin to scalable nested quantization.
• Score: 18.134147308944446
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: We propose a data-driven approach to explicitly learn the progressive encoding of a continuous source, which is successively decoded with increasing levels of quality and with the aid of correlated side information. This setup refers to the successive refinement of the Wyner-Ziv coding problem. Assuming ideal Slepian-Wolf coding, our approach employs recurrent neural networks (RNNs) to learn layered encoders and decoders for the quadratic Gaussian case. The models are trained by minimizing a variational bound on the rate-distortion function of the successively refined Wyner-Ziv coding problem. We demonstrate that RNNs can explicitly retrieve layered binning solutions akin to scalable nested quantization. Moreover, the rate-distortion performance of the scheme is on par with the corresponding monolithic Wyner-Ziv coding approach and is close to the rate-distortion bound.

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