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36 lines
1.8 KiB
Plaintext
36 lines
1.8 KiB
Plaintext
To extract the block costs, build Kvazaar as usual, and edit relevant
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parameters in the beginning of extract_rdcosts.py and run_filter.py, most
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importantly the number of cores and the set of video sequences you want to
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encode to extract costs. Run extract_rdcosts.py, it will use Kvazaar to encode
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each sequence and extract the costs measured there for the quantized blocks.
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The costs are stored compressed and sorted by block QP, in the following
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format:
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Size (B) | Description
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----------+------------
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4 | size: Coeff group size, in int16's
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4 | ccc: Coeff group's coding cost
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size * 2 | coeffs: Coeff group data
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To analyze the costs by running a linear regression over them, build the two
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tools using:
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$ gcc filter_rdcosts.c -O2 -o frcosts_matrix
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$ gcc ols_2ndpart.c -O2 -o ols_2ndpart
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Then run the regression in parallel by running run_filter.py. The reason to do
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it this way is because the data is stored compressed, so there is no way to
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mmap it in Matlab/Octave/something; the data sets are absolutely huge (larger
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than reasonable amounts of RAM in a decent workstation), but this way we can
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store the data compressed and process it in O(1) memory complexity, so it can
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be done as widely parallelized as you have CPU cores. The result files each
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consist of 4 numbers, which represent an approximate linear solution to the
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corresponding set of costs: the price in bits of a coefficient whose absolute
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value is a) 0, b) 1, c) 2, d) 3 or higher.
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After that, run rdcost_do_avg.py. It will calculate a per-QP average of the
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costs over the set of the sequences having been run (ie. for each QP, take the
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results for that QP for each sequence, and calculate their average). This data
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is what you can use to fill in the default_fast_coeff_cost_wts table in
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src/fast_coeff_cost.h.
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