Frequency-Selective Mesh-to-Grid Resampling for Image Communication

Koloda, Jan; Seiler, Jurgen; Kaup, André

doi:10.1109/tmm.2017.2683267

Cited by 9 publications

(10 citation statements)

References 46 publications

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“…For interpolation, the commonly used linear (LIN) [13] and cubic (CUB) [13] interpolation, as well as the Nadaraya-Watson Estimator (NWE) [14] are analyzed. Furthermore, results are shown for the already high performing FSMR [15]. Last, our approach, AFSMR [8], is analyzed in further detail.…”

Section: Qualitymentioning

confidence: 99%

Frame Rate Up-Conversion Using Key Point Agnostic Frequency-Selective Mesh-to-Grid Resampling

Heimann¹,

Spruck²,

Kaup³

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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High frame rates are desired in many fields of application. As in many cases the frame repetition rate of an already captured video has to be increased, frame rate up-conversion (FRUC) is of high interest. We conduct a motion compensated approach. From two neighboring frames, the motion is estimated and the neighboring pixels are shifted along the motion vector into the frame to be reconstructed. For displaying, these irregularly distributed mesh pixels have to be resampled onto regularly spaced grid positions. We use the model-based key point agnostic frequency-selective mesh-to-grid resampling (AFSMR) for this task and show that AFSMR works best for applications that contain irregular meshes with varying densities. AFSMR gains up to 3.2 dB in contrast to the already high performing frequency-selective mesh-to-grid resampling (FSMR). Additionally, AFSMR increases the run time by a factor of 11 relative to FSMR.

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Section: Qualitymentioning

confidence: 99%

Frame Rate Up-Conversion Using Key Point Agnostic Frequency-Selective Mesh-to-Grid Resampling

Heimann¹,

Spruck²,

Kaup³

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…Other reconstruction methods use Delaunay triangulation [9], second-order wavelets [10], or moving least squares [11] approaches to handle the irregularly sampling problem. Another method that can be used for resampling and reconstruction is the model-based Frequency-Selective Mesh-to-Grid Resampling (FSMR) [6]. It originates from frequencyselective reconstruction [12], which was developed over the last years [13], [14].…”

Section: State-of-the-artmentioning

confidence: 99%

“…FSMR is a versatile technique to handle any kind of scattered data interpolation problem. Furthermore, FSMR is a powerful technique that yields excellent resampling quality [6]. A great drawback of FSMR however is, that a huge data base is incorporated into model generation due to the pre-estimation of key points and, thereby, the run time is quite long.…”

Section: State-of-the-artmentioning

confidence: 99%

“…The spatial weighting is controlled by parameter α. As further explained in [6], α is chosen adaptively to the density of pixels in A. To obtain the expansion coefficient ĉ(ν) (k,l) , the weighted residual energy E (ν) can be obtained [6].…”

Section: State-of-the-artmentioning

confidence: 99%

“…They are disregarded using the newly proposed AFSMR. one of those state-of-the-art methods is named frequencyselective mesh-to-grid resampling (FSMR) [6]. To overcome the drawback of computational complexity and to enhance the resampling quality further, the novel key point agnostic FSMR (AFSMR) with spectral weighting is proposed.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Key Point Agnostic Frequency-Selective Mesh-to-Grid Image Resampling using Spectral Weighting

Heimann

Genser

Kaup

2020

2020 IEEE 22nd International Workshop on Multimedia Signal Processing (MMSP)

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Many applications in image processing require resampling of arbitrarily located samples onto regular grid positions. This is important in frame-rate up-conversion, superresolution, and image warping among others. A state-of-the-art high quality model-based resampling technique is frequencyselective mesh-to-grid resampling which requires pre-estimation of key points. In this paper, we propose a new key point agnostic frequency-selective mesh-to-grid resampling that does not depend on pre-estimated key points. Hence, the number of data points that are included is reduced drastically and the run time decreases significantly. To compensate for the key points, a spectral weighting function is introduced that models the optical transfer function in order to favor low frequencies more than high ones. Thereby, resampling artefacts like ringing are supressed reliably and the resampling quality increases. On average, the new AFSMR is conceptually simpler and gains up to 1.2 dB in terms of PSNR compared to the original mesh-to-grid resampling while being approximately 14.5 times faster.

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Frequency-Selective Geometry Upsampling of Point Clouds

Heimann

Spruck

Kaup

2022

2022 IEEE International Conference on Image Processing (ICIP)

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The demand for high-resolution point clouds has increased throughout the last years. However, capturing high-resolution point clouds is expensive and thus, frequently replaced by upsampling of lowresolution data. Most state-of-the-art methods are either restricted to a rastered grid, incorporate normal vectors, or are trained for a single use case. We propose to use the frequency selectivity principle, where a frequency model is estimated locally that approximates the surface of the point cloud. Then, additional points are inserted into the approximated surface. Our novel frequency-selective geometry upsampling shows superior results in terms of subjective as well as objective quality compared to state-of-the-art methods for scaling factors of 2 and 4. On average, our proposed method shows a 4.4 times smaller point-to-point error than the second best state-ofthe-art PU-Net for a scale factor of 4.

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Frequency-Selective Mesh-to-Grid Resampling for Image Communication

Cited by 9 publications

References 46 publications

Frame Rate Up-Conversion Using Key Point Agnostic Frequency-Selective Mesh-to-Grid Resampling

Frame Rate Up-Conversion Using Key Point Agnostic Frequency-Selective Mesh-to-Grid Resampling

Key Point Agnostic Frequency-Selective Mesh-to-Grid Image Resampling using Spectral Weighting

Frequency-Selective Geometry Upsampling of Point Clouds

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