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. On the last stage a correction filter is applied which fixes smoothing introduced on 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 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 0.95 correlation to each other, so they can be represented by any single element (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.
AVIR is devoted to women. Your digital photos can look good at any size!
C++ compiler and system with efficient "float" floating point (24-bit mantissa) type support. This library can also internally use the "double" and SIMD floating point types during resizing if needed. This library does not have dependencies beside the standard C library.
The image resizer is represented by the avir::CImageResizer<> class, which is a single front-end class for the whole library. You do not basically need to use nor understand any other classes beside this class.
The code of the library resides in the "avir" C++ namespace, effectively isolating it from all other code. The code is thread-safe. You need just a single resizer object per running application, at any time, even when resizing images concurrently.
To resize images in your application, simply add 3 lines of code:
- avir :: CImageResizer<> ImageResizer( 8 );
- ImageResizer.resizeImage( InBuf, 640, 480, 0, OutBuf, 1024, 768, 3, 0 ); (multi-threaded operation requires additional coding, see the documentation)
For low-ringing performance:
- avir :: CImageResizer<> ImageResizer( 8, 0, avir :: CImageResizerParamsLR() );
The library is able to process images of any bit depth: this includes 8-bit, 16-bit, float and double types. Larger integer and signed integer types are not supported. Supported source and destination image sizes are up to 2.1 gigapixels (46300x46300 or equivalent dimensions, e.g. 53467x40100).
The code of this library was commented in the Doxygen style. To generate the documentation locally you may run the "doxygen ./other/avirdoxy.txt" command from the library's directory. Note that the code was suitably documented allowing you to make modifications, and to gain full understanding of the algorithm.
Preliminary tests show that this library can resize 8-bit RGB 5184x3456 (17.9 Mpixel) image down to 1920x1280 (2.5 Mpixel) image in 566 milliseconds, utilizing a single thread, on a typical Intel Core i7-4770K processor-based system without overclocking. This scales down to 195 milliseconds if 4 threads are utilized. This time can be reduced further down to 148 milliseconds by utilizing SIMD floating point processing. This library's performance has a big potential to grow together with evolving processor architectures as currently performance is clearly limited by memory bandwidth, not by algorithm's mathematical operations and overhead.
Multi-threaded operation is not provided by this library "out of the box". The multi-threaded infrastructure is available, but requires additional system-specific interfacing code for engagement.
This library is capable of using SIMD floating point types for internal variables. This means that up to 4 color channels can be processed in parallel. For example, this gives 40% performance boost when resizing 3-channel images. Since the default interleaved processing algorithm itself remains non-SIMD, the use of SIMD internal types is not practical for 1-channel image resizing (due to overhead). SIMD internal type can be used this way:
- avir :: CImageResizer< avir :: fpclass_float4 > ImageResizer( 8 );
For 1-channel and 2-channel image resizing when AVX instructions are allowed it is reasonable to utilize de-interleaved SIMD processing algorithm. While it gives no performance benefit if the "float4" SSE processing type is used, it offers around 35% performance boost if the "float8" AVX processing type is used (given dithering is not performed, or otherwise performance is reduced at dithering stage since recursive dithering cannot be parallelized). The internal type remains non-SIMD "float". De-interleaved algorithm can be used this way:
- avir :: CImageResizer< avir :: fpclass_float8_dil > ImageResizer( 8 );
This library was tested for compatibility with GNU C++, Microsoft Visual C++ and Intel C++ compilers, on 32- and 64-bit Windows, Mac OS X and CentOS Linux. The code was also tested with Dr.Memory/Win32 for the absence of uninitialized or unaddressable memory accesses.
All code is fully "inline", without the need to compile any source files. The memory footprint of the library itself is very modest, except that the size of the temporary image buffers depends on the input and output image sizes, and is proportionally large.
The "heart" of resizing algorithm's quality resides in the parameters defined via the avir::CImageResizerParams structure. While the default set of parameters that offers a good quality was already provided, there is (probably) still a place for improvement exists, and the default parameters may change in a future update. If you need to recall an exact set of parameters, simply save them locally for a later use.
When the algorithm is run with no resizing applied (k=1), the result of resizing will not be an exact, but a very close copy of the source image. The reason for such inexactness is that the image is always low-pass filtered at first to reduce aliasing during subsequent resizing, and at last filtered by a correction filter. Such approach allows algorithm to maintain a stable level of quality regardless of the resizing "k" factor used.
This library includes a binary command line tool "imageresize" for major desktop platforms. This tool was designed to be used as a demonstration of library's performance and as a reference, it uses 4 threads and float4 SIMD internal type during processing. This tool uses the following libraries:
- turbojpeg Copyright (c) 2009-2013 D. R. Commander
- libpng Copyright (c) 1998-2013 Glenn Randers-Pehrson
- zlib Copyright (c) 1995-2013 Jean-loup Gailly and Mark Adler
The use of certain low-pass filters and 2X upsampling in this library is hardly debatable, because they are needed to attain a certain anti-aliasing effect and keep ringing artifacts low. But the use of sinc function-based interpolation filter that is 18 taps-long (may be higher, up to 36 taps in practice) can be questioned, because even in 0th order case such interpolation filter requires 18 multiply-add operations. Comparatively, an optimal Hermit or cubic interpolation spline requires 8 multiply and 11 add operations.
One of the reasons 18-tap filter is preferred, is because due to memory bandwidth limitations using a lower-order filter does not provide any significant performance increase (e.g. 14-tap filter is less than 5% more efficient overall). At the same time, in comparison to cubic spline, 18-tap filter embeds a low-pass filter that rejects signal above 0.5*pi (provides additional anti-aliasing filtering), and this filter has a consistent shape at all fractional offsets. Splines have a varying low-pass filter shape at different fractional offsets (e.g. no low-pass filtering at 0.0 offset, and maximal low-pass filtering at 0.5 offset). 18-tap filter also offers a superior stop-band attenuation which almost guarantees absence of artifacts if the image is considerably sharpened afterwards.
Classic approaches to image resizing do not perform an additional 2X upsizing. So, why such upsizing is needed at all in AVIR? Indeed, image resizing can be implemented using a single interpolation filter which is applied to the source image directly. However, such approach has limitations:
First of all, speaking about non-2X-upsized resizing, during upsizing the interpolation filter has to be tuned to a frequency close to pi (Nyquist) in order to reduce high-frequency smoothing: this reduces the space left for filter optimization. Beside that, during downsizing, a filter that performs well and predictable when tuned to frequencies close to the Nyquist frequency, may become distorted in its spectral shape when it is tuned to lower frequencies. That is why it is usually a good idea to have filter's stop-band begin below Nyquist so that the transition band's shape remains stable at any lower-frequency setting. At the same time, this requirement complicates a further corrective filtering, because correction filter may become too steep at the point where the stop-band beings.
Secondly, speaking about non-2X-upsized resizing, filter has to be very short (with a base length of 5-7 taps, further multiplied by the resizing factor) or otherwise the ringing artifacts will be very strong: it is a general rule that the steeper the filter is around signal frequencies being removed the higher the ringing artifacts. That is why it is preferred to move steep transitions into the spectral area with a quieter signal. A short filter also means it cannot provide a strong "beyond-Nyquist" stop-band attenuation, so an interpolated image will look a bit edgy or not very clean due to stop-band artifacts.
To sum up, only additional controlled 2X upsizing provides enough spectral space to design interpolation filter without visible ringing artifacts yet providing a strong stop-band attenuation and stable spectral characteristics (good at any resizing "k" factor). Moreover, 2X upsizing becomes very important in maintaining a good resizing quality when downsizing and upsizing by small "k" factors, in the range 0.5 to 2: resizing approaches that do not perform 2X upsizing usually cannot design a good interpolation filter for such factors just because there is not enough spectral space available.
First of all, AVIR is a general solution to image resizing problem. That is why it should not be directly compared to "spline interpolation" or "Lanczos resampling", because the latter two are only means to design interpolation filters, and they can be implemented in a variety of ways, even in sub-optimal ways. Secondly, with only a minimal effort AVIR can be changed to use any existing interpolation formula and any window function, but this is just not needed.
An effort was made to compare Peaked Cosine to Lanczos window function, and here is the author's opinion. Peaked Cosine has two degrees of freedom whereas Lanczos has one degree of freedom. While both functions can be used with acceptable results, Peaked Cosine window function used in automatic parameter optimization really pushes the limits of frequency response linearity, anti-aliasing strength (stop-band attenuation) and low-ringing performance which Lanczos cannot usually achieve. This is true at least when using a general-purpose downhill simplex optimization method. Lanczos window has good (but not better) characteristics in several special cases which makes it of limited use in a general solution such as AVIR.
Among other window functions (Kaiser, Gaussian, Cauchy, Poisson, generalized cosine windows) there are no better candidates as well. It looks like Peaked Cosine function's scalability (it retains stable, almost continously-variable spectral characteristics at any window parameter values), and its ability to create "desirable" pass-band ripple in the frequency response near the cutoff point contribute to its better overall quality. Somehow Peaked Cosine window function optimization manages to converge to reasonable states in most cases (that is why AVIR library comes with a set of equally robust, but distinctive parameter sets) whereas all other window functions tend to produce unpredictable optimization results.
The only disadvantage of Peaked Cosine window function is that usable filters windowed by this function tend to be longer than "usual" (with Kaiser window being the "golden standard" for filter length per decibel of stop-band attenuation). This is a price that should be paid for stable spectral characteristics.
This library is used by:
Please drop me a note at [email protected] and I will include a link to your software product to the list of users. This list is important at maintaining confidence in this library among the interested parties.