Interpreting CNN for Low Complexity Learned Sub-pixel Motion Compensation in Video Coding

• URL: http://arxiv.org/abs/2006.06392v1
• Date: Thu, 11 Jun 2020 13:10:20 GMT
• Title: Interpreting CNN for Low Complexity Learned Sub-pixel Motion Compensation in Video Coding
• Authors: Luka Murn, Saverio Blasi, Alan F. Smeaton, Noel E. O'Connor, Marta Mrak
• Abstract summary: A novel neural network-based tool is presented which improves the complexity of reference samples needed for fractional precision compensation motion. When the approach is implemented in the Versatile Video Coding (VVC) test model, up to 4.5% BD-rate saving for individual sequences is achieved. The complexity learned is significantly reduced compared to the application of full neural network.
• Score: 16.381904711953947
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Deep learning has shown great potential in image and video compression tasks. However, it brings bit savings at the cost of significant increases in coding complexity, which limits its potential for implementation within practical applications. In this paper, a novel neural network-based tool is presented which improves the interpolation of reference samples needed for fractional precision motion compensation. Contrary to previous efforts, the proposed approach focuses on complexity reduction achieved by interpreting the interpolation filters learned by the networks. When the approach is implemented in the Versatile Video Coding (VVC) test model, up to 4.5% BD-rate saving for individual sequences is achieved compared with the baseline VVC, while the complexity of learned interpolation is significantly reduced compared to the application of full neural network.

Related papers

• Joint Hierarchical Priors and Adaptive Spatial Resolution for Efficient Neural Image Compression [11.25130799452367]
  We propose an absolute image compression transformer (ICT) for neural image compression (NIC) ICT captures both global and local contexts from the latent representations and better parameterize the distribution of the quantized latents. Our framework significantly improves the trade-off between coding efficiency and decoder complexity over the versatile video coding (VVC) reference encoder (VTM-18.0) and the neural SwinT-ChARM.
  arXiv  Detail & Related papers  (2023-07-05T13:17:14Z)
• Progressive Fourier Neural Representation for Sequential Video Compilation [75.43041679717376]
  Motivated by continual learning, this work investigates how to accumulate and transfer neural implicit representations for multiple complex video data over sequential encoding sessions. We propose a novel method, Progressive Fourier Neural Representation (PFNR), that aims to find an adaptive and compact sub-module in Fourier space to encode videos in each training session. We validate our PFNR method on the UVG8/17 and DAVIS50 video sequence benchmarks and achieve impressive performance gains over strong continual learning baselines.
  arXiv  Detail & Related papers  (2023-06-20T06:02:19Z)
• VNVC: A Versatile Neural Video Coding Framework for Efficient Human-Machine Vision [59.632286735304156]
  It is more efficient to enhance/analyze the coded representations directly without decoding them into pixels. We propose a versatile neural video coding (VNVC) framework, which targets learning compact representations to support both reconstruction and direct enhancement/analysis.
  arXiv  Detail & Related papers  (2023-06-19T03:04:57Z)
• NAF: Neural Attenuation Fields for Sparse-View CBCT Reconstruction [79.13750275141139]
  This paper proposes a novel and fast self-supervised solution for sparse-view CBCT reconstruction. The desired attenuation coefficients are represented as a continuous function of 3D spatial coordinates, parameterized by a fully-connected deep neural network. A learning-based encoder entailing hash coding is adopted to help the network capture high-frequency details.
  arXiv  Detail & Related papers  (2022-09-29T04:06:00Z)
• Neural Data-Dependent Transform for Learned Image Compression [72.86505042102155]
  We build a neural data-dependent transform and introduce a continuous online mode decision mechanism to jointly optimize the coding efficiency for each individual image. The experimental results show the effectiveness of the proposed neural-syntax design and the continuous online mode decision mechanism.
  arXiv  Detail & Related papers  (2022-03-09T14:56:48Z)
• Reducing Redundancy in the Bottleneck Representation of the Autoencoders [98.78384185493624]
  Autoencoders are a type of unsupervised neural networks, which can be used to solve various tasks. We propose a scheme to explicitly penalize feature redundancies in the bottleneck representation. We tested our approach across different tasks: dimensionality reduction using three different dataset, image compression using the MNIST dataset, and image denoising using fashion MNIST.
  arXiv  Detail & Related papers  (2022-02-09T18:48:02Z)
• End-to-end Neural Video Coding Using a Compound Spatiotemporal Representation [33.54844063875569]
  We propose a hybrid motion compensation (HMC) method that adaptively combines the predictions generated by two approaches. Specifically, we generate a compoundtemporal representation (STR) through a recurrent information aggregation (RIA) module. We further design a one-to-many decoder pipeline to generate multiple predictions from the CSTR, including vector-based resampling, adaptive kernel-based resampling, compensation mode selection maps and texture enhancements.
  arXiv  Detail & Related papers  (2021-08-05T19:43:32Z)
• Improved CNN-based Learning of Interpolation Filters for Low-Complexity Inter Prediction in Video Coding [5.46121027847413]
  This paper introduces a novel explainable neural network-based inter-prediction scheme. A novel training framework enables each network branch to resemble a specific fractional shift. When implemented in the context of the Versatile Video Coding (VVC) test model, 0.77%, 1.27% and 2.25% BD-rate savings can be achieved.
  arXiv  Detail & Related papers  (2021-06-16T16:48:01Z)
• A Deep-Unfolded Reference-Based RPCA Network For Video Foreground-Background Separation [86.35434065681925]
  This paper proposes a new deep-unfolding-based network design for the problem of Robust Principal Component Analysis (RPCA) Unlike existing designs, our approach focuses on modeling the temporal correlation between the sparse representations of consecutive video frames. Experimentation using the moving MNIST dataset shows that the proposed network outperforms a recently proposed state-of-the-art RPCA network in the task of video foreground-background separation.
  arXiv  Detail & Related papers  (2020-10-02T11:40:09Z)
• Neural Video Coding using Multiscale Motion Compensation and Spatiotemporal Context Model [45.46660511313426]
  We propose an end-to-end deep neural video coding framework (NVC) It uses variational autoencoders (VAEs) with joint spatial and temporal prior aggregation (PA) to exploit the correlations in intra-frame pixels, inter-frame motions and inter-frame compensation residuals. NVC is evaluated for the low-delay causal settings and compared with H.265/HEVC, H.264/AVC and the other learnt video compression methods.
  arXiv  Detail & Related papers  (2020-07-09T06:15:17Z)
• Analytic Simplification of Neural Network based Intra-Prediction Modes for Video Compression [10.08097582267397]
  This paper presents two ways to derive simplified intra-prediction from learnt models. It shows that these streamlined techniques can lead to efficient compression solutions.
  arXiv  Detail & Related papers  (2020-04-23T10:25:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.