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Thread: 31 Pngs Corpus

  1. #1
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    31 Pngs Corpus

    I have created from my personal photographs a simple and small test corpus that covers much of the needs I had for butteraugli tuning. I believe others can find this test corpus interesting, too. This corpus should be suitable for testing both lossless and lossy compression algorithms.

    https://drive.google.com/drive/folde...Fk?usp=sharing

    This corpus differs from many other test corpora by having less pixels (more information per pixel), less noise, less compression and image reconstruction artefacts. Some of the images are lightly unsharp masked -- before they were subsampled.

    All these photos are under the Creative Commons CC0 license. That means you can copy, modify, distribute any of these photos, even for commercial purposes, all without asking permission.

  2. Thanks (4):

    Cyan (4th November 2016),Malloc Voidstar (3rd November 2016),pothos2 (3rd November 2016),Turtle (4th November 2016)

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    Just my simple opinion: Maybe a little heavy on flower closeups

  4. Thanks:

    Jyrki Alakuijala (3rd November 2016)

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    Quote Originally Posted by SvenBent View Post
    Just my simple opinion: Maybe a little heavy on flower closeups
    Thank you for the analysis! Yes, 15½ out of the 31 are pictures of flowers. The reasons for closeup flowers are simple:

    1) Experience from existing systems, particularly yuv420 on the Kodak corpus showed that flowers often bring up the artefacts, particularly the red-green boundaries. Something like red-flowers.png or rgb.png gets completely destroyed by yuv420.

    2) I added a lot of flowers because there are high frequency details that often are easy for the human visual system but difficult for lossy compressors. Flower petals often have long low intensity structures that fall into this category.

    3) Also, there is no superposition in the eye; sometimes a blue change that can be observed in red background cannot be seen in a green background -- it is important to have such features in a plenty of different background colors. It is typical for lossy compressors to do particularly badly on red -- however, to maximize compression one should do equally badly in every corner of the colorspace, i.e., the test data should have similar coverage everywhere.

    4) The red rose picture was proven quite useful in detecting red degradation in an image compressor, so I rotated rgb planes in it and created a green and a blue rose for this purpose. Typically compressors have less difficulty with green and blue, but having them measured allows to have consistent performance.

    5) Closeup includes a transition from high-frequency rich areas into low frequency rich areas, i.e., where the detail and the blurry background meet. Ideally the lossy compressor preserves this transition without blurrying the details, and without adding new details (artefacts) to the background.

    6) Flower petals often have subtle multi-frequency low-intensity multi-color textures with gradients, repeated (or fractal) structure, complemented with high contrast points in a large variety of dc color.

    Most of the images I chose with some particular lossy compression aspect in mind.

  6. Thanks:

    SvenBent (4th November 2016)

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