avir | quality pro image resizing / scaling C library , image | Computer Vision library

avir is a C++ library typically used in Artificial Intelligence, Computer Vision applications. avir has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

Keywords: image resize, image resizer, image resizing, image scaling, image scaler, image resize c++, image resizer c++. Me, Aleksey Vaneev, is happy to offer you an open source image resizing / scaling library which has reached a production level of quality, and is ready to be incorporated into any project. This library features routines for both down- and upsizing of 8- and 16-bit, 1 to 4-channel images. Image resizing routines were implemented in multi-platform C++ code, and have a high level of optimality. Beside resizing, this library offers a sub-pixel shift operation. Built-in sRGB gamma correction is available. The resizing algorithm at first produces 2X upsized image (relative to the source image size, or relative to the destination image size if downsizing is performed) and then performs interpolation using a bank of sinc function-based fractional delay filters. At the last stage a correction filter is applied which fixes smoothing introduced at previous steps. The resizing algorithm was designed to provide the best visual quality. The author even believes this algorithm provides the "ultimate" level of quality (for an orthogonal, non neural-network, resizing) which cannot be increased further: no math exists to provide a better frequency response, better anti-aliasing quality and at the same time having less ringing artifacts: these are 3 elements that define any resizing algorithm's quality; in AVIR practice these elements have a high correlation to each other, so they can be represented by a single parameter (AVIR offers several parameter sets with varying quality). Algorithm's time performance turned out to be very good as well (for the "ultimate" image quality). An important element utilized by this algorithm is the so called Peaked Cosine window function, which is applied over sinc function in all filters. Please consult the documentation for more details. Note that since AVIR implements orthogonal resizing, it may exhibit diagonal aliasing artifacts. These artifacts are usually suppressed by EWA or radial filtering techniques. EWA-like technique is not implemented in AVIR, because it requires considerably more computing resources and may produce a blurred image. As a bonus, a faster LANCIR image resizing algorithm is also offered as a part of this library. But the main focus of this documentation is the original AVIR image resizing algorithm. AVIR does not offer affine and non-linear image transformations "out of the box". Since upsizing is a relatively fast operation in AVIR (required time scales linearly with the output image area), affine and non-linear transformations can be implemented in steps: 4- to 8-times upsizing, transformation via bilinear interpolation, downsizing (linear proportional affine transformations can probably skip the downsizing step). This should not compromise the transformation quality much as bilinear interpolation's problems will mostly reside in spectral area without useful signal, with a maximum of 0.7 dB high-frequency attenuation for 4-times upsizing, and 0.17 dB attenuation for 8-times upsizing. This approach is probably as time efficient as performing a high-quality transform over the input image directly (the only serious drawback is the increased memory requirement). Note that affine transformations that change image proportions should first apply proportion change during upsizing. P.S. Please credit the author of this library in your documentation in the following way: "AVIR image resizing algorithm designed by Aleksey Vaneev".