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Award ID contains: 2048042

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  1. ; (, IEEE Journal on Selected Areas in Communications)
    Free, publicly-accessible full text available July 1, 2026
  2. ; (, IEEE)
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  3. ; ; (, IEEE)
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  4. ; (, IFAC-PapersOnLine)
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  5. ; (, IEEE)
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  6. ; (, IEEE)
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  7. ; (, IEEE)
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  8. ; (, 2023 IEEE Statistical Signal Processing Workshop (SSP))
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  9. ; (, 2023 IEEE International Symposium on Information Theory (ISIT))
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  10. ; (, 2022 Data Compression Conference (DCC))
    This paper is concerned with the quantization setting where the encoder and the decoder have misaligned objectives. We first motivate the problem via a toy example which demonstrates the intricacies of the strategic quantization problem, specifically shows that iterative optimization of the decoder and the encoder mappings may not converge to a local optimum. As a remedy, we propose a dynamic programming based optimal optimization method, inspired by the early works in the quantization theory. We then extend our approach to variable-rate (entropy-coded) quantization. We finally present numerical results obtained via the proposed algorithms. 
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