Analytic Simplification of Neural Network based Intra-Prediction Modes for Video Compression

• URL: http://arxiv.org/abs/2004.11056v1
• Date: Thu, 23 Apr 2020 10:25:54 GMT
• Title: Analytic Simplification of Neural Network based Intra-Prediction Modes for Video Compression
• Authors: Maria Santamaria, Saverio Blasi, Ebroul Izquierdo, Marta Mrak
• Abstract summary: 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.
• Score: 10.08097582267397
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the increasing demand for video content at higher resolutions, it is evermore critical to find ways to limit the complexity of video encoding tasks in order to reduce costs, power consumption and environmental impact of video services. In the last few years, algorithms based on Neural Networks (NN) have been shown to benefit many conventional video coding modules. But while such techniques can considerably improve the compression efficiency, they usually are very computationally intensive. It is highly beneficial to simplify models learnt by NN so that meaningful insights can be exploited with the goal of deriving less complex solutions. This paper presents two ways to derive simplified intra-prediction from learnt models, and shows that these streamlined techniques can lead to efficient compression solutions.

Related papers

• Hierarchical Patch Diffusion Models for High-Resolution Video Generation [50.42746357450949]
  We develop deep context fusion, which propagates context information from low-scale to high-scale patches in a hierarchical manner. We also propose adaptive computation, which allocates more network capacity and computation towards coarse image details. The resulting model sets a new state-of-the-art FVD score of 66.32 and Inception Score of 87.68 in class-conditional video generation.
  arXiv  Detail & Related papers  (2024-06-12T01:12:53Z)
• Dynamic Semantic Compression for CNN Inference in Multi-access Edge Computing: A Graph Reinforcement Learning-based Autoencoder [82.8833476520429]
  We propose a novel semantic compression method, autoencoder-based CNN architecture (AECNN) for effective semantic extraction and compression in partial offloading. In the semantic encoder, we introduce a feature compression module based on the channel attention mechanism in CNNs, to compress intermediate data by selecting the most informative features. In the semantic decoder, we design a lightweight decoder to reconstruct the intermediate data through learning from the received compressed data to improve accuracy.
  arXiv  Detail & Related papers  (2024-01-19T15:19:47Z)
• Efficient Meta-Tuning for Content-aware Neural Video Delivery [40.3731358963689]
  We present Efficient Meta-Tuning (EMT) to reduce the computational cost. EMT adapts a meta-learned model to the first chunk of the input video. We propose a novel sampling strategy to extract the most challenging patches from video frames.
  arXiv  Detail & Related papers  (2022-07-20T06:47:10Z)
• A Coding Framework and Benchmark towards Low-Bitrate Video Understanding [63.05385140193666]
  We propose a traditional-neural mixed coding framework that takes advantage of both traditional codecs and neural networks (NNs) The framework is optimized by ensuring that a transportation-efficient semantic representation of the video is preserved. We build a low-bitrate video understanding benchmark with three downstream tasks on eight datasets, demonstrating the notable superiority of our approach.
  arXiv  Detail & Related papers  (2022-02-06T16:29:15Z)
• 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)
• Towards Transparent Application of Machine Learning in Video Processing [3.491870689686827]
  Machine learning techniques for more efficient video compression and video enhancement have been developed thanks to breakthroughs in deep learning. New techniques typically come in the form of resource-hungry black-boxes (overly complex with little transparency regarding the inner workings) The aim of this work is to understand and optimise learned models in video processing applications so systems that incorporate them can be used in a more trustworthy manner.
  arXiv  Detail & Related papers  (2021-05-26T17:24:23Z)
• Low-Fidelity End-to-End Video Encoder Pre-training for Temporal Action Localization [96.73647162960842]
  TAL is a fundamental yet challenging task in video understanding. Existing TAL methods rely on pre-training a video encoder through action classification supervision. We introduce a novel low-fidelity end-to-end (LoFi) video encoder pre-training method.
  arXiv  Detail & Related papers  (2021-03-28T22:18:14Z)
• Attention-Based Neural Networks for Chroma Intra Prediction in Video Coding [13.638411611516172]
  This work focuses on reducing the complexity of attention-based architectures for chroma intra-prediction. A novel size-agnostic multi-model approach is proposed to reduce the complexity of the inference process. A collection of simplifications is presented in this paper, to further reduce the complexity overhead of the proposed prediction architecture.
  arXiv  Detail & Related papers  (2021-02-09T18:01:22Z)
• Interpreting CNN for Low Complexity Learned Sub-pixel Motion Compensation in Video Coding [16.381904711953947]
  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.
  arXiv  Detail & Related papers  (2020-06-11T13:10:20Z)
• Neural Network Compression Framework for fast model inference [59.65531492759006]
  We present a new framework for neural networks compression with fine-tuning, which we called Neural Network Compression Framework (NNCF) It leverages recent advances of various network compression methods and implements some of them, such as sparsity, quantization, and binarization. The framework can be used within the training samples, which are supplied with it, or as a standalone package that can be seamlessly integrated into the existing training code.
  arXiv  Detail & Related papers  (2020-02-20T11:24:01Z)
• Learning End-to-End Lossy Image Compression: A Benchmark [90.35363142246806]
  We first conduct a comprehensive literature survey of learned image compression methods. We describe milestones in cutting-edge learned image-compression methods, review a broad range of existing works, and provide insights into their historical development routes. By introducing a coarse-to-fine hyperprior model for entropy estimation and signal reconstruction, we achieve improved rate-distortion performance.
  arXiv  Detail & Related papers  (2020-02-10T13:13:43Z)

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.