Results for Compression

"Learning-based point cloud (PC) compression is a promising research avenue to reduce the transmission and storage costs for PC applications. Existing learning-based methods to compress PCs attributes employ variational autoencoders (VAE) or normalizing flows (NF) to learn compact signal representations. However, VAEs leverage a lower-dimensional bottleneck that ...

Achieving successful variable bitrate compression with computationally simple algorithms from a single end-to-end learned image or video compression model remains a challenge. Many approaches have been proposed, including conditional auto-encoders, channel-adaptive gains for the latent tensor or uniformly quantizing all elements of the latent tensor. This paper f ...

Representation of 3D scenes is gaining popularity in industry, notably for Virtual Reality, Augmented Reality, and 360° Video. The point cloud format is well suited for such representations. Indeed, point clouds can be created with a simple capture process and modest processing, enabling a real-time, end-to-end point cloud distribution chain. However, point cloud ...

This paper describes a light field scalable compression scheme based on the sparsity of the angular Fourier transform of the light field. A subset of sub-aperture images (or views) is compressed using HEVC as a base layer and transmitted to the decoder. An entire light field is reconstructed from this view subset using a method exploiting the sparsity of the ligh ...

In recent video coding standards, the introduction of multiple transform selection (MTS) has significantly improved coding efficiency. The latest standard, Versatile Video Coding (VVC), adopted implicit MTS as an alternative tool to explicit MTS. This tool reduces the encoder complexity by inferring the optimal transform type from decoder-side information instead ...

Deep bi-prediction blending. This paper presents a learning-based method to improve bi-prediction in video coding. In conventional video coding solutions, block-based motion compensation blocks from already decoded reference pictures stand out as the main tool used to predict the current frame. Especially, bi-predicted blocks, i.e. blocks that combine two differe ...

Despite many modern applications of Deep Neural Networks (DNNs), the large number of parameters in the hidden layers makes them unattractive for deployment on devices with storage capacity constraints. In this paper we propose a Data-Driven Low-rank (DDLR) method to reduce the number of parameters of pretrained DNNs and expedite inference by imposing low-rank str ...

"JVET has developed a new Enhanced Compression Model (ECM) for testing future video coding algorithms on top of the Versatile Video Coding (VVC) standard. Reference Picture Resampling (RPR) is a powerful tool that improves video coding efficiency of next generation like Versatile Video Coding (VVC). This feature is well designed to support frame changing resoluti ...

"The last standard Versatile Video Codec (VVC), aims to im- prove the compression efficiency by saving around 50% of bitrate at the same quality compared to its predecessor High Efficiency Video Codec (HEVC). However, this comes with a significant rise in computational complexity due to the new added tools in the encoder side. This paper proposes a speed- up tech ...