arXiv subjects

Rahul Mohideen Kaja Mohideen

Publications and source records attributed to Rahul Mohideen Kaja Mohideen.

4 recordsLinked to original sources

arXiv2024

Image Compression with Isotropic and Anisotropic Shepard Inpainting

Inpainting-based codecs store sparse selected pixel data and decode by reconstructing the discarded image parts by inpainting. Successful codecs (coders and decoders) traditionally use inpainting operators that solve partial differential equations. This requires some numerical expertise if efficient implementations are necessary. Our goal is to investigate variants of Shepard inpainting as simple alternatives for inpainting-based compression. They can be implemented efficiently when we localise their weighting function. To turn them into viable codecs, we have to introduce novel extensions of classical Shepard interpolation that adapt successful ideas from previous codecs: Anisotropy allows direction-dependent inpainting, which improves reconstruction quality. Additionally, we incorporate data selection by subdivision as an efficient way to tailor the stored information to the image structure. On the encoding side, we introduce the novel concept of joint inpainting and prediction for isotropic Shepard codecs, where storage cost can be reduced based on intermediate inpainting results. In an ablation study, we show the usefulness of these individual contributions and demonstrate that they offer synergies which elevate the performance of Shepard inpainting to surprising levels. Our resulting approaches offer a more favourable trade-off between simplicity and quality than traditional inpainting-based codecs. Experiments show that they can outperform JPEG and JPEG2000 at high compression ratios.

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arXiv2021

A Systematic Evaluation of Coding Strategies for Sparse Binary Images

Inpainting-based compression represents images in terms of a sparse subset of its pixel data. Storing the carefully optimised positions of known data creates a lossless compression problem on sparse and often scattered binary images. This central issue is crucial for the performance of such codecs. Since it has only received little attention in the literature, we conduct the first systematic investigation of this problem so far. To this end, we first review and compare a wide range of existing methods from image compression and general purpose coding in terms of their coding efficiency and runtime. Afterwards, an ablation study enables us to identify and isolate the most useful components of existing methods. With context mixing, we combine those ingredients into new codecs that offer either better compression ratios or a more favourable trade-off between speed and performance.

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arXiv2021

Inpainting-based Video Compression in FullHD

Compression methods based on inpainting are an evolving alternative to classical transform-based codecs for still images. Attempts to apply these ideas to video compression are rare, since reaching real-time performance is very challenging. Therefore, current approaches focus on simplified frame-by-frame reconstructions that ignore temporal redundancies. As a remedy, we propose a highly efficient, real-time capable prediction and correction approach that fully relies on partial differential equations (PDEs) in all steps of the codec: Dense variational optic flow fields yield accurate motion-compensated predictions, while homogeneous diffusion inpainting is applied for intra prediction. To compress residuals, we introduce a new highly efficient block-based variant of pseudodifferential inpainting. Our novel architecture outperforms other inpainting-based video codecs in terms of both quality and speed. For the first time in inpainting-based video compression, we can decompress FullHD (1080p) videos in real-time with a fully CPU-based implementation, outperforming previous approaches by roughly one order of magnitude.

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arXiv2020

Compressing Colour Images with Joint Inpainting and Prediction

Inpainting-based codecs store sparse, quantised pixel data directly and decode by interpolating the discarded image parts. This interpolation can be used simultaneously for efficient coding by predicting pixel data to be stored. Such joint inpainting and prediction approaches yield good results with simple components such as regular grids and Shepard interpolation on grey value images, but they lack a dedicated mode for colour images. Therefore, we evaluate different approaches for inpainting-based colour compression. Inpainting operators are able to reconstruct a large range of colours from a small colour palette of the known pixels. We exploit this with a luma preference mode which uses higher sparsity in YCbCr colour channels than in the brightness channel. Furthermore, we propose the first full vector quantisation mode for an inpainting-based codec that stores only a small codebook of colours. Our experiments reveal that both colour extensions yield significant improvements.

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