Progressive space frequency quantization for SAR data compression

Gleich, Dušan; Planinšič, P.; Gergič, B.; Čučej, Ž.

doi:10.1109/36.981344

Cited by 11 publications

(3 citation statements)

References 16 publications

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“…The Space Frequency Quantization [3] (SFQ) is an effective method for the compression of images, which optimally prunes the wavelet coefficients in zerotree structure. The SFQ method was shown as an effective method for image compression [4] in the combination with Trellis Coded Quantization [5] (TCQ). The combination of the SFQ and the TCQ method provides the best results in the rate distortion sense in the literature for the optical images.…”

Section: Introductionmentioning

confidence: 99%

“…The combination of the SFQ and the TCQ method provides the best results in the rate distortion sense in the literature for the optical images. We have introduced the progressive SFQ method [5] adapted for the SAR data compression, which consists of the SFQ and the progressive context based coding of the bit plane bits. In this paper we have combined the SFQ method and the context based modeling of the wavelet coefficients presented in [6].…”

Section: Introductionmentioning

confidence: 99%

“…In this paper we have combined the SFQ method and the context based modeling of the wavelet coefficients presented in [6]. A new method uses fuzzy context modeling an progressive transmission of the pruned wavelet coefficients and the best RD performance among the wavelet coders [2], [5], [6] tested on the SAR data.…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy coded space frequency quantization

Gleich

Planinšič

Čučej

IEEE International Geoscience and Remote Sensing Symposium

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In this paper we propose a new image coding technique, which consists of the Space Frequency Quantization (SFQ), and a fuzzy context based modeling. The Space Frequency Quantization (SFQ) is an effective coding technique, which uses a zerotree pruning of wavelet coefficients. After the Space Frequency quantization is applied to the wavelet coefficients the non-pruned coefficients are quantized using context modeling. The context based quantization technique uses the partitioning proposed by the Trellis Coded Quantization and the fuzzy logic to predict the next TCQ state from the context. Quantized indices are entropy coded using an arithmetic coder. The probability estimation is based on the observation of the past coded bits. The results of the proposed compression scheme showed that the proposed coder outperforms the state-of-the-art coders in the RD sense for the SAR data compression.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%