Rescale

Overview

The Rescale algorithm is used to scale the input image by means of resampling its content to make it conform to the output image dimensions.

No pre-filtering is applied, it's expected that the input content doesn't have frequencies higher than the Nyquist limit to avoid aliasing artifacts when downsampling.

Several interpolation methods are available, allowing trade-offs between quality and performance.

Input	Factor	Output
	\begin{align*} f_x &= 2/3 \\[5pt] f_y &= 3/2 \end{align*}

Implementation

For every output pixel, calculate the corresponding input pixel using the formula:

\[ \mathit{out}[x,y] = P(f_x x, f_y y) \]

where P depends on the interpolation method used.

Sampling operation considers that whole coordinates fall on pixel center.

C API functions

For list of limitations, constraints and backends that implements the algorithm, consult reference documentation of the following functions:

Function	Description
vpiSubmitRescale	Changes the size and scale of a 2D image.

Usage

Language: C/C++ Python

1. Import VPI module

   import vpi

2. Rescale the VPI image input so that output has 66% of the original width and height is increased 50%, all using the CUDA backend. It's using the linear interpolation.

   with vpi.Backend.CUDA:
       output = input.rescale((input.width*2//3, input.height*3//2), interp=vpi.Interp.LINEAR, border=vpi.Border.ZERO)

1. Initialization phase
   1. Include the header that defines the rescale function.

      #include <vpi/algo/Rescale.h>

   2. Define the input image object.

          VPIImage input = /*...*/;

   3. Create an output image with the new required size and same format as input.

          int32_t w, h;
          vpiImageGetSize(input, &w, &h);
       
          VPIImageFormat type;
          vpiImageGetFormat(input, &type);
       
          VPIImage output;
          vpiImageCreate(w * 2 / 3.0f, h * 3 / 2.0f, type, 0, &output);

   4. Create the stream where the algorithm will be submitted for execution.

          VPIStream stream;
          vpiStreamCreate(0, &stream);

2. Processing phase
   1. Submit the algorithm to the stream along with all parameters. It'll be executed by the CUDA algorithm.

          vpiSubmitRescale(stream, VPI_BACKEND_CUDA, input, output, VPI_INTERP_LINEAR, VPI_BORDER_ZERO, 0);

   2. Optionally, wait until the processing is done.

          vpiStreamSync(stream);

3. Cleanup phase
   1. Free resources held by the stream and the input and output images.

          vpiStreamDestroy(stream);
          vpiImageDestroy(input);
          vpiImageDestroy(output);

Consult the Rescale for a complete example.

For more information, see Rescale in the "C API Reference" section of VPI - Vision Programming Interface.

Performance

For information on how to use the performance table below, see Algorithm Performance Tables.
Before comparing measurements, consult Comparing Algorithm Elapsed Times.
For further information on how performance was benchmarked, see Performance Benchmark.

clear filters Device: Jetson AGX Xavier Jetson AGX Orin  -  Streams: 1 2 4 8

References

• Daniel Ruijters, Bart M. ter Romeny, Paul Suetens (2008) "Efficient GPU-Based Texture Interpolation using Uniform B-Spline"
  Journal of Graphics Tools, 13:4 61-69.