A Nonaliasing, Real-Time Spatial Transform Technique

Fant, Karl M.

doi:10.1109/mcg.1986.276613

Cited by 80 publications

(21 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We gathered a dataset of 500 000 uncompressed images -a set of 100 different image resolutions logarithmically distributed by size in the range [0.24; 24] megapixels. These images were resampled down from larger photographs to the desired size using the Fant method [6], which is a geometrically accurate anti-aliasing technique. The original images of different sizes from 24 MP to approximately 36 MP were collected from a wide selection of image-hosting websites and personal archives.…”

Section: Methodsmentioning

confidence: 99%

Predicting and Optimizing Image Compression

Мурашко

Thomson

Leather

2016

Proceedings of the 24th ACM International Conference on Multimedia

View full text Add to dashboard Cite

Image compression is a core task for mobile devices, social media and cloud storage backend services. Key evaluation criteria for compression are: the quality of the output, the compression ratio achieved and the computational time (and energy) expended. Predicting the effectiveness of standard compression implementations like libjpeg and WebP on a novel image is challenging, and often leads to non-optimal compression. This paper presents a machine learning-based technique to accurately model the outcome of image compression for arbitrary new images in terms of quality and compression ratio, without requiring significant additional computational time and energy. Using this model, we can actively adapt the aggressiveness of compression on a per image basis to accurately fit user requirements, leading to a more optimal compression.

show abstract

Section: Methodsmentioning

confidence: 99%

Predicting and Optimizing Image Compression

Мурашко

Thomson

Leather

2016

Proceedings of the 24th ACM International Conference on Multimedia

View full text Add to dashboard Cite

show abstract

“…Catmull-Rom spline interpolation allows for smooth interpolation between two different meshes to determine the correspondence between pixels. Fant's algorithm (Fant, 1986) is used to resample the pixel color according to the spatial mapping provided by Catmull-Rom spline interpolation.…”

Section: Interpolation Stepmentioning

confidence: 99%

A Fast Morphing-Based Interpolation for Medical Images: Application to Conformal Radiotherapy

Atoui

Miguet²,

Sarrut³

2011

Image Anal Stereol

View full text Add to dashboard Cite

A method is presented for fast interpolation between medical images. The method is intended for both slice and projective interpolation. It allows offline interpolation between neighboring slices in tomographic data. Spatial correspondence between adjacent images is established using a block matching algorithm. Interpolation of image intensities is then carried out by morphing between the images. The morphing-based method is compared to standard linear interpolation, block-matching-based interpolation and registrationbased interpolation in 3D tomographic data sets. Results show that the proposed method scored similar performance in comparison to registration-based interpolation, and significantly outperforms both linear and block-matching-based interpolation. This method is applied in the context of conformal radiotherapy for online projective interpolation between Digitally Reconstructed Radiographs (DRRs).

show abstract

“…In first phase, we begin by fitting vertical Bezier curve [5] through x-coordinates of the control points and applying Fant's resampling algorithm [1] horizontally to resample the original image in second phase. And mathematically a parametric Bezier Curve is defined by…”

Section: A First Passmentioning

confidence: 99%