Sampling versus random binning for multiple descriptions of a bandlimited source

Adam Mashiach, Jan Østergaard, Ram Zamir

نتاج البحث: فصل من :كتاب / تقرير / مؤتمرمنشور من مؤتمرمراجعة النظراء

ملخص

Random binning is an efficient, yet complex, coding technique for the symmetric L-description source coding problem. We propose an alternative approach, that uses the quantized samples of a bandlimited source as 'descriptions'. By the Nyquist condition, the source can be reconstructed if enough samples are received. We examine a coding scheme that combines sampling and noise-shaped quantization for a scenario in which only K <L descriptions or all L descriptions are received. Some of the received K-sets of descriptions correspond to uniform sampling while others to non-uniform sampling. This scheme achieves the optimum rate-distortion performance for uniform-sampling K-sets, but suffers noise amplification for nonuniform-sampling K-sets. We then show that by increasing the sampling rate and adding a random-binning stage, the optimal operation point is achieved for any K-set.

اللغة الأصليةالإنجليزيّة
عنوان منشور المضيف2013 IEEE Information Theory Workshop, ITW 2013
المعرِّفات الرقمية للأشياء
حالة النشرنُشِر - 2013
الحدث2013 IEEE Information Theory Workshop, ITW 2013 - Seville, أسبانيا
المدة: ٩ سبتمبر ٢٠١٣١٣ سبتمبر ٢٠١٣

سلسلة المنشورات

الاسم2013 IEEE Information Theory Workshop, ITW 2013

!!Conference

!!Conference2013 IEEE Information Theory Workshop, ITW 2013
الدولة/الإقليمأسبانيا
المدينةSeville
المدة٩/٠٩/١٣١٣/٠٩/١٣

All Science Journal Classification (ASJC) codes

  • !!Information Systems

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