Deprecation Notice: Vship is moving away from github (microslop).

Here is the link to the new Codeberg Repository This git repository will receive no further commits/releases but files will remain for now.

Vship : Fast Metric Computation on GPU

An easy to use high-performance Library for GPU-accelerated visual fidelity metrics with SSIMULACRA2, Butteraugli & CVVDP.

Overview

vship provides hardware-accelerated implementations of:

• SSIMULACRA2: A perceptual image quality metric from Cloudinary's SSIMULACRA2
• Butteraugli: Google's psychovisual image difference metric from libjxl
• CVVDP: University of Cambridge's psychovisual video quality metric

The plugin uses HIP/CUDA for GPU acceleration, providing significant performance improvements over CPU implementations. It can be used with a simple binary (FFVship), as a vapoursynth plugin and has a C API.

There are precompiled binaries ready to be used in the release section.

Projects Featuring Vship

If you want to use Vship with a pre-defined workflow, here are some projects featuring Vship:

• Av1an: A Cross-platform command-line AV1 / VP9 / HEVC / H264 encoding framework with per scene quality encoding
• xav: A simpler alternative to Av1an dedicated to Target Quality Encoding trying to be as fast as possible.
• SSIMULACRApy: A Python script to compare videos and output their SSIMU2 scores using various metrics (by Kosaka)
• metrics: A perceptual video metrics toolkit for video encoder developers (by the Psychovisual Experts Group)
• vs_align: A vapoursynth plugin measuring temporal offset between two videos
• chunknorris: A python script to adjust the quality encoding parameters at each scene of a video base on objective metrics
• Media-Metrologist: Media-Metrologist is a library for measuring video quality using a suite of metrics on a per-scene and per-frame basis.
• lvsfunc: JET project containing various functions to help with video processing.

Installation

The steps to build vship from source are provided below. See Compilation and Usage for more details. For compiling on Windows, see FFVship Windows Compilation.

Dependencies

For all build options the following are required:

• make
• hipcc (AMD HIP SDK) or nvcc (NVIDIA CUDA SDK)

Additionally, to build the FFvship cli tool:

• ffms2 (and libavutil header to compile)
• pkg-config

Build Instructions

1. Use the appropriate target for your gpu or use case.

#libvship Build
make buildcuda     # Build for the current systems Nvidia gpu
make buildcudaall  # Build for all supported Nvidia gpus
make build         # Build for the current systems AMD gpu
make buildall      # Build for all supported AMD gpus

#FFVship CLI linux tool build (requires libvship built before FFVSHIP)
make buildFFVSHIP

2. Install libvship and eventually the FFVship executable.

The install target automatically detects and installs only the components that were built.

make install
#for arch, you need to use another prefix:
make install PREFIX=/usr

Library Usage

FFVship

To control the performance-to-VRAM trade-off, set the -g argument in FFVship to control the number of GPU threads to use. You can also control the number of decoder threads with -t. I recommend 4 GPU threads as a good compromise between performance and VRAM usage.

This contains only some of the numerous options offered by Vship I recommend checking the doc or using -h to get the full list.

usage: ./FFVship [-h] [--source SOURCE] [--encoded ENCODED]
                    [-m {SSIMULACRA2, Butteraugli, CVVDP}]
                    [--start start] [--end end] [-e --every every]
                    [-t THREADS] [-g gpuThreads] [--gpu-id gpu_id]
                    [--json OUTPUT]
                    [--list-gpu]

Vapoursynth

To control the performance-to-VRAM trade-off, set the numStream argument to control how many GPU threads to use. I recommend 4 as a good compromise between both.

SSIMULACRA2

See SSIMULACRA2 for details like calculating VRAM usage.

import vapoursynth as vs
core = vs.core

# Load reference and distorted clips
ref = core.bs.VideoSource("reference.mp4")
dist = core.bs.VideoSource("distorted.mp4")

# Calculate SSIMULACRA2 scores
#numStream controls the performance-to-VRAM trade-off
result = ref.vship.SSIMULACRA2(dist, numStream = 4)

# Extract scores from frame properties
scores = [frame.props["_SSIMULACRA2"] for frame in result.frames()]

# Print average score
print(f"Average SSIMULACRA2 score: {sum(scores) / len(scores)}")

Butteraugli

See BUTTERAUGLI for more details like calculating VRAM usage.

import vapoursynth as vs
core = vs.core

# Load reference and distorted clips
ref = core.bs.VideoSource("reference.mp4")
dist = core.bs.VideoSource("distorted.mp4")

# Calculate Butteraugli scores
# distmap controls whether to return a visual distortion map or the reference clip
# intensity_multiplier controls sensitivity
# qnorm controls which Norm to calculate and return in _BUTTERAUGLI_QNorm
result = ref.vship.BUTTERAUGLI(dist, distmap = 0, numStream = 4, qnorm = 5)

# Extract scores from frame properties (three different norms available)
scores_3norm = [frame.props["_BUTTERAUGLI_3Norm"] for frame in result.frames()]
scores_infnorm = [frame.props["_BUTTERAUGLI_INFNorm"] for frame in result.frames()]
scores_5norm = [frame.props["_BUTTERAUGLI_QNorm"] for frame in result.frames()]

# Alternatively get all scores in one pass
all_scores = [frame.props["_BUTTERAUGLI_3Norm"],
               frame.props["_BUTTERAUGLI_INFNorm"],
               frame.props["_BUTTERAUGLI_QNorm"]]
              for frame in result.frames()]

# Print average scores
print(f"Average Butteraugli 3Norm distance: {sum(scores_3norm) / len(scores_3norm)})
print(f"Average Butteraugli MaxNorm distance: {sum(scores_infnorm) / len(scores_infnorm)})
print(f"Average Butteraugli 5Norm distance: {sum(scores_5norm) / len(scores_5norm)})

# Output grayscale visualation of distortion for visual analysis
ref.vship.BUTTERAUGLI(dist, distmap = 1, numStream = 4).set_output()

CVVDP

See CVVDP for more details like calculating VRAM usage.

import vapoursynth as vs
core = vs.core

# Load reference and distorted clips
ref = core.bs.VideoSource("reference.mp4")
dist = core.bs.VideoSource("distorted.mp4")

# Calculate CVVDP scores
# distmap controls whether to return a visual distortion map or the reference clip
# model_name controls which Display Model to use
# resizeToDisplay conrols whether or not to resize the reference and distorted inputs to the Display Model resolution
result = ref.vship.CVVDP(dist, distmap = 0, model_name = "standard_fhd", resizeToDisplay = 0)

# Extract scores from frame properties
scores = [frame.props["_CVVDP"] for frame in result.frames()]

# Only use the last score of CVVDP. (it takes into account every frame that it has seen up to now)
#it is different because it is an actually temporal metric unlike others
print(f"CVVDP Video Score: {scores[-1]}")

# Output grayscale visualation of distortion for visual analysis
ref.vship.CVVDP(dist, distmap = 1).set_output()

Performance

Testing Hardware: Ryzen 7940HS + RTX 4050 Mobile (strong CPU - weak GPU configuration) Testing Clip: 1080p 1339 frames

SSIMU2 Implementation	HW Type	Time
JXL	CPU	115s
VSZIP	CPU	68s
Vapoursynth Vship	GPU	25s
FFVship	GPU	9s

Butteraugli Implementation	HW Type	Time
JXL	CPU	239s
Vapoursynth Vship	GPU	47s
FFVship	GPU	37s

CVVDP Implementation	HW Type	Score	Time
Original Repo	GPU	9.4808	162s
FFVship	GPU	9.52268	22s

vship dramatically outperforms CPU-based and GPU-based implementations of these metrics while preserving a high degree of accuracy.

References

• Butteraugli Source Code: libjxl/libjxl
• SSIMULACRA2 Source Code: cloudinary/ssimulacra2
• CVVDP Source Code: gfxdisp/ColorVideoVDP

Credits

Special thanks to dnjulek for the Zig-based SSIMULACRA2 implementation in vszip.

License

This project is licensed under the MIT license. License information is provided by the LICENSE.