Hybrid Neural Network Coding for Adaptive Image and Video Compression
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Solution Overview
Problem
Conventional image and video codecs, such as H.264, H.265, and H.266, are inefficient in compressing digital images and video data, as they rely on block-based methods that require manual tuning and do not leverage the data-driven potential of neural networks for optimal compression.
Innovation Solution
Hybrid neural network-based codecs are introduced, combining block-based encoders with neural network tools, allowing for selective use of neural network encoders based on rate-distortion analysis, separate processing of luma and chroma components, and multi-stage encoding with checkerboard patterns, to enhance compression efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional block-based codecs are used, then device complexity is reduced and ease of manufacture is improved, but compression efficiency deteriorates and productivity decreases
Solution Approach 1:
The image is divided into multiple coding blocks that are processed independently by the neural network encoder. Each block undergoes separate encoding operations including transformation, quantization, and entropy coding, allowing parallel processing and modular implementation that manages complexity while improving overall compression efficiency
Solution Approach 2:
A hybrid architecture is introduced that combines conventional block-based coding tools with neural network-based encoding. The system uses an intermediary layer that selects between conventional and neural network encoding methods for different blocks, allowing the benefits of both approaches while managing device complexity through selective application
2Adaptability or versatility
If manual tuning of block-based methods is used, then ease of operation is improved, but adaptability deteriorates and compression performance is limited
Solution Approach 1:
The neural network encoder is trained offline on large datasets to automatically learn optimal encoding strategies for different types of content. During operation, the pre-trained network automatically adapts to different image characteristics without requiring manual tuning, achieving both high adaptability and ease of operation through self-learning capabilities
Solution Approach 2:
The system dynamically adjusts encoding parameters such as block size, transformation type, and quantization strength based on the statistical properties of the input image data. The neural network analyzes local characteristics and automatically modifies encoding parameters to optimize compression for each specific region, achieving data-driven adaptability
3Loss of information
If neural network encoders are used exclusively, then compression efficiency is improved, but loss of information increases and image quality deteriorates
Solution Approach 1:
The encoding strategy is made dynamic by allowing the system to switch between conventional and neural network encoding methods based on the characteristics of each coding block. For blocks where neural network encoding provides significant compression benefits, the neural method is applied; for blocks where conventional methods perform adequately, the simpler method is used, optimizing the trade-off between compression performance and information loss
Solution Approach 2:
The system incorporates rate-distortion optimization that uses feedback from the encoding process to adjust subsequent encoding decisions. The neural network encoder evaluates the trade-off between compression ratio and reconstruction quality, and this feedback is used to control the encoding strength and select appropriate encoding methods for different regions, minimizing information loss while maintaining compression performance
4Quantity of substance
If conventional codecs are used, then processing speed is maintained, but compression ratio deteriorates and bit rate increases
Solution Approach 1:
The image is partitioned into multiple coding blocks that can be processed in parallel by the neural network encoder. This segmentation allows the computationally intensive neural network operations to be distributed across multiple processing units, maintaining processing speed while achieving superior compression ratios and lower bit rates through the intelligent encoding of each block
Data Source
AI summary
Improved neural-network-based image and video coding techniques are presented, including hybrid techniques that include both tools of a host codec and neural-network-based tools. In these improved techniques, the host coding tools may include conventional video coding standards such H.266 (VVC). In an aspects, source frames may be partitioned and either host or neural-network-based tools may be selected per partition. Coding parameter decisions for a partition may be constrained based on the partitioning and coding tool selection. Rate control for host and neural network tools may be combined. Multi-stage processing of neural network output may use a checkerboard prediction pattern.