From Optimised Inpainting with Linear PDEs Towards Competitive Image Compression Codecs

Peter, Pascal; Hoffmann, Sebastian; Nedwed, Frank; Hoeltgen, Laurent; Weickert, Joachim

doi:10.1007/978-3-319-29451-3_6

Cited by 11 publications

(7 citation statements)

References 19 publications

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“…Even though most diffusion-type partial differential equations can be used to reconstruct the data, previous works have shown that homogeneous, linear diffusion ranks among the best performing ones [26] in the compression context. We also refer to [2] for a more general overview on PDE-based inpainting methods.…”

Section: Partial Differential Equation Based Image Compressionmentioning

confidence: 99%

“…These efforts have also been exploited for lossy image compression schemes that can nowadays compete with other state-of-the-art approaches [28,31]. Even simple methods that rely on the interpolation capabilities of the Laplacian may yield more accurate reconstructions than JPEG 2000 [26]. Unfortunately, the underlying optimisation problem concerned with selecting optimal point locations is computationally intensive.…”

Section: Introductionmentioning

confidence: 99%

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Clustering-based quantisation for PDE-based image compression

Hoeltgen

Peter

Breuß

2017

SIViP

Self Cite

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Finding optimal data for inpainting is a key problem in the context of partial differential equation based image compression. The data that yields the most accurate reconstruction is real-valued. Thus, quantisation models are mandatory to allow an efficient encoding. These can also be understood as challenging data clustering problems. Although clustering approaches are well suited for this kind of compression codecs, very few works actually consider them. Each pixel has a global impact on the reconstruction and optimal data locations are strongly correlated with their corresponding colour values. These facts make it hard to predict which feature works best.In this paper we discuss quantisation strategies based on popular methods such as k-means. We are lead to the central question which kind of feature vectors are best suited for image compression. To this end we consider choices such as the pixel values, the histogram or the colour map.Our findings show that the number of colours can be reduced significantly without impacting the reconstruction quality. Surprisingly, these benefits do not directly translate to a good image compression performance. The gains in the compression ratio are lost due to increased storage costs. This suggests that it is integral to evaluate the clustering on both, the reconstruction error and the final file size.

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Section: Partial Differential Equation Based Image Compressionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Clustering-based quantisation for PDE-based image compression

Hoeltgen

Peter

Breuß

2017

SIViP

Self Cite

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“…A potential future research direction is employing more advanced interpolation methods, similar to [41], which can further improve quality of the final result.…”

Section: Discussionmentioning

confidence: 99%

Efficient Segmentation: Learning Downsampling Near Semantic Boundaries

Marin

Vajda

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

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Many automated processes such as auto-piloting rely on a good semantic segmentation as a critical component. To speed up performance, it is common to downsample the input frame. However, this comes at the cost of missed small objects and reduced accuracy at semantic boundaries. To address this problem, we propose a new content-adaptive downsampling technique that learns to favor sampling locations near semantic boundaries of target classes. Costperformance analysis shows that our method consistently outperforms the uniform sampling improving balance between accuracy and computational efficiency. Our adaptive sampling gives segmentation with better quality of boundaries and more reliable support for smaller-size objects.

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“…[14,15,31]. For the latter, strategies based on the Laplacian are often advocated [5,23,28,32]. Mathematically, the simplest model is given by the elliptic mixed boundary value problem…”

Section: Image Inpainting With Pdesmentioning

confidence: 99%

Towards PDE-Based Video Compression with Optimal Masks Prolongated by Optic Flow

2020

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Lossy image compression methods based on partial differential equations have received much attention in recent years. They may yield high-quality results but rely on the computationally expensive task of finding an optimal selection of data. For the possible extension to video compression, this data selection is a crucial issue. In this context, one could either analyse the video sequence as a whole or perform a frame-by-frame optimisation strategy. Both approaches are prohibitive in terms of memory and run time. In this work, we propose to restrict the expensive computation of optimal data to a single frame and to approximate the optimal reconstruction data for the remaining frames by prolongating it by means of an optic flow field. In this way, we achieve a notable decrease in the computational complexity. As a proof-of-concept, we evaluate the proposed approach for multiple sequences with different characteristics. In doing this, we discuss in detail the influence of possible computational setups. We show that the approach preserves a reasonable quality in the reconstruction and is very robust against errors in the flow field.

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From Optimised Inpainting with Linear PDEs Towards Competitive Image Compression Codecs

Cited by 11 publications

References 19 publications

Clustering-based quantisation for PDE-based image compression

Clustering-based quantisation for PDE-based image compression

Efficient Segmentation: Learning Downsampling Near Semantic Boundaries

Towards PDE-Based Video Compression with Optimal Masks Prolongated by Optic Flow

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