LYCOS RETRIEVER
Entropy Encoding: Codes
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The basic steps in H.264 encoding are prediction, transform and quantization, and entropy encoding. Prediction takes advantage of spatial and temporal data redundancies in video frames to greatly reduce the amount of data to be encoded. Transform and quantization further compact the data by applying mathematical techniques that express the energy in the predicted video as a matrix of frequency coefficients, many of which will be zero. Finally, entropy encoding substitutes binary codes for strings of repeating coefficients to achieve the final, compressed bitstream.
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The final step in the JPEG encoding process is entropy encoding. The JPEG specification allows for either arithmetic or Huffman encoding. It is generally acknowledged that arithmetic encoding performs marginally better for some images, but is much more complex to implement. Also, there are currently patent problems with using arithmetic encoding, so most implementors steer clear of it. Huffman encoding is in the public domain and can be used without worry of patent infringement. Consequently, Huffman encoding is utilized in most JPEG implementations and in the CAL code.
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This unit divides the quantized sub-bands into rectangular code blocks of up to 32 x 32 samples to make them ready for entropy encoding. The BA112JPEG2000E IP core features a configurable number of entropy encoders placed in parallel configuration to sustain high encoding rates. Each entropy chain processes a code block independently from neighbor chains. The tile splitter module arbitrates between the available chains, dispatching the various code blocks to be encoded. The tile splitter stores the code blocks into local code block buffers.
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A simple approach to coding a pair of stereo images together is to jointly encode the corresponding pairs of pixels using an optimal entropy code. This is ... equivalent to jointly coding the left image pixel and the difference between the right and left image pixels.
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