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# Benchmarking
For speed benchmarks on single images in single or multi-threaded decoding
`djxl` can print decoding speed information. See `djxl --help` for details
on the decoding options and note that the output image is optional for
benchmarking purposes.
For a more comprehensive comparison of compression density between multiple
options, the tool `benchmark_xl` can be used (see below).
## Benchmarking with benchmark_xl
We recommend `build/tools/benchmark_xl` as a convenient method for reading
images or image sequences, encoding them using various codecs (jpeg jxl png
webp), decoding the result, and computing objective quality metrics. An example
invocation is:
```bash
build/tools/benchmark_xl --input "/path/*.png" --codec jxl:wombat:d1,jxl:cheetah:d2
```
Multiple comma-separated codecs are allowed. The characters after : are
parameters for the codec, separated by colons, in this case specifying maximum
target psychovisual distances of 1 and 2 (higher implies lower quality) and
the encoder effort (see below). Other common parameters are `r0.5` (target
bitrate 0.5 bits per pixel) and `q92` (quality 92, on a scale of 0-100, where
higher is better). The `jxl` codec supports the following additional parameters:
Speed: `lightning`, `thunder`, `falcon`, `cheetah`, `hare`, `wombat`, `squirrel`,
`kitten`, `tortoise` control the encoder effort in ascending order. This also
affects memory usage: using lower effort will typically reduce memory consumption
during encoding.
* `lightning` and `thunder` are fast modes useful for lossless mode (modular).
* `falcon` disables all of the following tools.
* `cheetah` enables coefficient reordering, context clustering, and heuristics
for selecting DCT sizes and quantization steps.
* `hare` enables Gaborish filtering, chroma from luma, and an initial estimate
of quantization steps.
* `wombat` enables error diffusion quantization and full DCT size selection
heuristics.
* `squirrel` (default) enables dots, patches, and spline detection, and full
context clustering.
* `kitten` optimizes the adaptive quantization for a psychovisual metric.
* `tortoise` enables a more thorough adaptive quantization search.
Mode: JPEG XL has two modes. The default is Var-DCT mode, which is suitable for
lossy compression. The other mode is Modular mode, which is suitable for lossless
compression. Modular mode can also do lossy compression (e.g. `jxl:m:q50`).
* `m` activates modular mode.
Other arguments to benchmark_xl include:
* `--save_compressed`: save codestreams to `output_dir`.
* `--save_decompressed`: save decompressed outputs to `output_dir`.
* `--output_extension`: selects the format used to output decoded images.
* `--num_threads`: number of codec instances that will independently
encode/decode images, or 0.
* `--inner_threads`: how many threads each instance should use for parallel
encoding/decoding, or 0.
* `--encode_reps`/`--decode_reps`: how many times to repeat encoding/decoding
each image, for more consistent measurements (we recommend 10).
The benchmark output begins with a header:
```
Compr Input Compr Compr Compr Decomp Butteraugli
Method Pixels Size BPP # MP/s MP/s Distance Error p norm BPP*pnorm Errors
```
`ComprMethod` lists each each comma-separated codec. `InputPixels` is the number
of pixels in the input image. `ComprSize` is the codestream size in bytes and
`ComprBPP` the bitrate. `Compr MP/s` and `Decomp MP/s` are the
compress/decompress throughput, in units of Megapixels/second.
`Butteraugli Distance` indicates the maximum psychovisual error in the decoded
image (larger is worse). `Error p norm` is a similar summary of the psychovisual
error, but closer to an average, giving less weight to small low-quality
regions. `BPP*pnorm` is the product of `ComprBPP` and `Error p norm`, which is a
figure of merit for the codec (lower is better). `Errors` is nonzero if errors
occurred while loading or encoding/decoding the image.
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