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34 lines
1.3 KiB
Plaintext
34 lines
1.3 KiB
Plaintext
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Build Kvazaar as usual with make, then edit extract_rdcosts.py so that the
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parameters suit your usage (the directories, num of threads and Kvazaar
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params) and then run extract_rdcosts.py. It will run a lot of Kvazaar
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instances in parallel to encode a lot of videos and sift off all the coeff
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groups they measure RD cost for. The coeff groups will be written into the
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relevant data file in the following format (although through GZIP):
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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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You can roll your own filter_rdcosts.c program to analyze the data the way
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you want, and run it like:
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$ gzip -d < /path/to/compressed_datafile.gz | ./filter_rdcosts | less
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Maybe one day, there'll be a multithreaded script like extract_rdcosts.py to
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automate and parallelize processing of a massive heap of data files.
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EDIT:
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It's now possible to do OLS regression by streaming the source data twice
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from source and using Octave to invert the temporary result matrix, and
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that's what run_filter.py does in parallel. To do this on data you've
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gathered by extract_rdcosts.py:
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$ gcc filter_rdcosts.c -o frcosts_matrix
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$ gcc ols_2ndpart.c -o ols_2ndpart
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$ ./run_filter.py
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Although you should probably adjust the run_filter.py params before actually
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running it
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