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Motion modeling plays a central role in video compression. This role is even more critical in fisheye video sequences
since the wide-angle fisheye imagery has special characteristics as in exhibiting radial distortion. While the translational motion model employed by modern video coding standards, such as HEVC, is sufficient in most cases, using higher order models is
beneficial; for this reason, the upcoming video coding standard, VVC, employs a 4-parameter affine model. Discrete cosine basis

Screen content videos (SCVs) are becoming popular in many applications. Compared with natural content videos (NCVs), the SCVs have different characteristics. Therefore, the screen content coding (SCC) based on HEVC adopts some new coding tools (intra block copy and palette mode etc.) to improve coding efficiency, but these tools increase the computational complexity as well. In this paper, we propose to predict the CU partition of the SCVs by a convolutional neural network

Inpainting-based compression has been suggested as a qualitative competitor to the JPEG family of transform-based codecs, specifically for high compression ratios. However, it also requires sophisticated interpolation, data optimisation and encoding tasks that are both slow and hard to implement. We propose a fast and simple alternative that combines Shepard interpolation with a novel joint inpainting and prediction approach. It represents the image by a fraction of its pixel values on a sparse regular subgrid that are selected by an efficient optimisation strategy.

Lossy image compression aims to encode images with a low bit-rate representation while preserving a pleasant visual quality of decompressed images. By utilizing the manually designed features, the traditional compression may not be suitable for diverse image content and may cause visible artifacts under the low bit rate constraint. Recently, deep learning based methods, which can extract the compact representation of an image in an auto-encoder way, were proposed for image compression.

The problem of impulse noise mitigation is considered when videos encoded using a SoftCast based Linear Video Coding scheme are transmitted using an OFDM scheme over a wideband channel prone to impulse noise A Fast Bayesian Matching Pursuit algorithm is employed for impulse noise mitigation This approach requires the provisioning of some OFDM subchannels to estimate the impulse noise locations and amplitudes Provisioned subchannels cannot be used to transmit data and lead to a decrease of the nominal decoded video quality at receivers in absence of impulse noise Using a phenomenological mod

Presentation Slides for "Convolutional Neural Networks for Video Intra Prediction Using Cross-component Adaptation"

We propose a deep semantic segmentation-based layered image compression (DSSLIC) framework in which the segmentation map of the input image is obtained and encoded as the base layer of the bit-stream. Experimental results show that the proposed framework outperforms the H.265/HEVC-based BPG and other codecs in both PSNR and MS-SSIM metrics in RGB domain. Besides, since semantic map is included in the bit-stream, the proposed scheme can facilitate many other tasks such as image search and object-based adaptive image compression.

Scalable lossless video coding is an important aspect for many professional applications. Wavelet-based video coding decomposes an input sequence into a lowpass and a highpass subband by filtering along the temporal axis. The lowpass subband can be used for previewing purposes, while the highpass subband provides the residual content for lossless reconstruction of the original sequence. The recursive application of the wavelet transform to the lowpass subband of the previous stage yields coarser temporal resolutions of the input sequence.

Video transrating has become an essential task in streaming service providers that need to transmit and deliver different versions of the same content for a multitude of users that operate under different network conditions. As the transrating operation is comprised of a decoding and an encoding step in sequence, a huge computational cost is required in such large-scale services, especially when considering the use of complex state-of-the-art codecs, such as the High Efficiency Video Coding (HEVC).

In video coding frameworks, the essence of intra coding is leveraging the spatial correlation within a frame to remove redundancy thus achieving compact transmitting data. With modern video acquisition devices improvement, more high-definition videos emerge into people’s lives which has set a new challenge for high-efficiency video coding. In this paper, we propose a novel intra video coding scheme based on Multiple Linear Regression (MLR), named Multiple linear regression Intra Prediction (MIP).