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The SPIHT algorithm encodes wavelet coefficients bit plane by bit plane from the most significant bit plane to the least significant bit plane.
Homologous coefficients are then encoded, bit plane by bit plane, using a suitable turbo code.
With the received bit plane values of sub-image 11, we can match the bit plane values of pred0 with the received bit plane value.
An error image is compressed using grayscale bit plane coding using quadtree method.
In this paper, we propose a bit plane modification (BPM) algorithm to achieve further performance improvement.
(5) Over the resulting quantization symbol stream, bit plane extraction is performed to form the bit plane arrays which are then independently turbo encoded.
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This leads us to adopt a multi-resolution and bit plane-based scalable approach for the compression.
Step 2. For eight-bit plane, we extracted n-bit plane to generate (512 times 512) size CDMBP image while embedded bits E are generated randomly.
This bit-plane encoder inherently targets parallel processing architecture.
When the bit-plane fails to decode, the erroneous bit-plane is still used to update the correlation channel estimate.
Instead of encoding the bit-planes progressively, the bit-plane encoding process can be finished in one pass when multiple bit-plane encoders are activated concurrently.
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