IEEE Trans Image Process - Multiple Description Coding With Randomly and Uniformly Offset Quantizers.

Tópicos

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Resumo

In this paper, two multiple description coding schemes are developed, based on prediction-induced randomly offset quantizers and unequal-deadzone-induced near-uniformly offset quantizers, respectively. In both schemes, each description encodes one source subset with a small quantization stepsize, and other subsets are predictively coded with a large quantization stepsize. In the first method, due to predictive coding, the quantization bins that a coefficient belongs to in different descriptions are randomly overlapped. The optimal reconstruction is obtained by finding the intersection of all received bins. In the second method, joint dequantization is also used, but near-uniform offsets are created among different low-rate quantizers by quantizing the predictions and by employing unequal deadzones. By generalizing the recently developed random quantization theory, the closed-form expression of the expected distortion is obtained for the first method, and a lower bound is obtained for the second method. The schemes are then applied to lapped transform-based multiple description image coding. The closed-form expressions enable the optimization of the lapped transform. An iterative algorithm is also developed to facilitate the optimization. Theoretical analyzes and image coding results show that both schemes achieve better performance than other methods in this category.

Resumo Limpo

paper two multipl descript code scheme develop base predictioninduc random offset quantiz unequaldeadzoneinduc nearuniform offset quantiz respect scheme descript encod one sourc subset small quantiz stepsiz subset predict code larg quantiz stepsiz first method due predict code quantiz bin coeffici belong differ descript random overlap optim reconstruct obtain find intersect receiv bin second method joint dequant also use nearuniform offset creat among differ lowrat quantiz quantiz predict employ unequ deadzon general recent develop random quantiz theori closedform express expect distort obtain first method lower bound obtain second method scheme appli lap transformbas multipl descript imag code closedform express enabl optim lap transform iter algorithm also develop facilit optim theoret analyz imag code result show scheme achiev better perform method categori

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