{"title":"基于自适应非减法抖动的非均匀量化器线性化","authors":"Morriel Kasher, P. Spasojevic, M. Tinston","doi":"10.1109/CISS56502.2023.10089625","DOIUrl":null,"url":null,"abstract":"Non-subtractive dithering is an effective method to improve quantizer performance by injecting input noise that reduces statistical correlations between input signals and quantization error. Existing non-subtractive dither theory has primarily designed dither signal distributions for linear, uniform quantizers, neglecting real-world non-idealities including non-uniformity and finite-level saturation. We develop a generalized analytical condition to guarantee independence of the quantization error moments from the input signal for an arbitrary finite-level non-linear quantizer characteristic. We use this to propose a novel asymmetric, adaptive dither technique for effective linearization of non-uniform quantizers via reduction of the first conditional quantization error moment. These adaptive dither distributions are shown to completely eliminate the first error moment in Lloyd-Max quantizers and significantly reduce it in non-linear quantizers. This allows the use of time-averaging to converge to an arbitrarily precise signal estimate in non-uniform quantizers.","PeriodicalId":243775,"journal":{"name":"2023 57th Annual Conference on Information Sciences and Systems (CISS)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Linearization of Non-Uniform Quantizers via Adaptive Non-Subtractive Dithering\",\"authors\":\"Morriel Kasher, P. Spasojevic, M. Tinston\",\"doi\":\"10.1109/CISS56502.2023.10089625\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Non-subtractive dithering is an effective method to improve quantizer performance by injecting input noise that reduces statistical correlations between input signals and quantization error. Existing non-subtractive dither theory has primarily designed dither signal distributions for linear, uniform quantizers, neglecting real-world non-idealities including non-uniformity and finite-level saturation. We develop a generalized analytical condition to guarantee independence of the quantization error moments from the input signal for an arbitrary finite-level non-linear quantizer characteristic. We use this to propose a novel asymmetric, adaptive dither technique for effective linearization of non-uniform quantizers via reduction of the first conditional quantization error moment. These adaptive dither distributions are shown to completely eliminate the first error moment in Lloyd-Max quantizers and significantly reduce it in non-linear quantizers. This allows the use of time-averaging to converge to an arbitrarily precise signal estimate in non-uniform quantizers.\",\"PeriodicalId\":243775,\"journal\":{\"name\":\"2023 57th Annual Conference on Information Sciences and Systems (CISS)\",\"volume\":\"88 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-03-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 57th Annual Conference on Information Sciences and Systems (CISS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CISS56502.2023.10089625\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 57th Annual Conference on Information Sciences and Systems (CISS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CISS56502.2023.10089625","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Linearization of Non-Uniform Quantizers via Adaptive Non-Subtractive Dithering
Non-subtractive dithering is an effective method to improve quantizer performance by injecting input noise that reduces statistical correlations between input signals and quantization error. Existing non-subtractive dither theory has primarily designed dither signal distributions for linear, uniform quantizers, neglecting real-world non-idealities including non-uniformity and finite-level saturation. We develop a generalized analytical condition to guarantee independence of the quantization error moments from the input signal for an arbitrary finite-level non-linear quantizer characteristic. We use this to propose a novel asymmetric, adaptive dither technique for effective linearization of non-uniform quantizers via reduction of the first conditional quantization error moment. These adaptive dither distributions are shown to completely eliminate the first error moment in Lloyd-Max quantizers and significantly reduce it in non-linear quantizers. This allows the use of time-averaging to converge to an arbitrarily precise signal estimate in non-uniform quantizers.