vmaf

New features

• Updated VMAF algorithm to v0.6.1, including:
  • Added a custom model for cellular phone screen viewing
  • Trained using new dataset, covering more difficult content
  • Elementary metric fixes: ADM behavior at near-black frames, motion behavior at scene boundaries
  • Compressed quality score range by 20% to accommodate higher dynamic range
  • Use MLE instead of DMOS as subjective model
• Added command line ffmpeg2vmaf, which takes encoded videos (instead of raw YUV) as input.
• Allow specifying crop and pad parameter in dataset files.
• Speeded up VMAF convolution operation by AVX.
• Added Travis continuous integration.
• Add implementation of KFLK - quality metric evaluation method based on AUC. Refer to: L. Krasula, K. Fliegel, P. Le Callet, M.Klima, "On the accuracy of objective image and video quality models: New methodology for performance evaluation", QoMEX 2016.
• Add options to use custom subjective models in run_vmaf_training and run_testing commands.

Bug fixes

• Revamped process-level parallelization for Executor.
• Fixed vmafossexec memory leakage.
• Fixed command line run_testing issue. Add command line test cases.
• Fixed a bug in DatasetReader.to_aggregated_dataset_file.
• Issue #36: SSIM and MS-SSIM sometimes get negative values.

New features

• Added a FAQ page.
• Added Xcode project support.
• Added support for docker usage (#30).
• Update CLIs with support for JSON/XML output, temporal pooling options and etc.
• Added wrapper/vmafossexec -- a Python-independent executable in C++.
• Added SSIM/MS-SSIM option in run_testing.
• Updated VmafFeatureExtractor to 0.2.2b with scaled ADM features exposed (adm_scale0-3).
• Updated VMAF algorithm to v0.3.2 (with numerical fixes in the features).

New features

• Add SsimFeatureExtractor and MsSsimFeatureExtractor with intermediate features (luminence, contrast, structure).
• Refactor feature code to expose ssim/ms-ssim, speed up ssim/ms-ssim.
• Updated VMAF algorithm to v0.3.1.