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This paper presents a novel method for a data-driven training of
affine-linear predictors which perform intra prediction in state-ofthe-
art video coding. The main aspect of our training design is the
use of subband decomposition of both the input and the output of the
prediction. Due to this architecture, the same set of predictors can be
shared across different block shapes leading to a very limited memory
requirement. Also, the computational complexity of the resulting
predictors can be limited such that it does not exceed the complexity

This paper presents an efficient method for encoding common projection formats in 360◦ video coding, in which we exploit inactive regions. These regions are ignored in the reconstruction of the equirectangular format or the viewport in virtual reality applications. As the content of these pixels is irrelevant, we neglect the corresponding pixel values in ratedistortion optimization, residual transformation, as well as inloop filtering and achieve bitrate savings of up to 10%.

High-dynamic-range (HDR) video streams have been delivered through high-efficiency video coding (HEVC). HDR video tone mapping is required additionally but is operated separately to adjust the content’s dynamic range for each display device. HDR video tone mapping and HEVC encoding share common computational processes for spatial and temporal coherence in a video stream; however, they have been developed and implemented independently with their own computational budgets.

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.