Neural Video Codec Hybrid Entropy Modeling With Flexible Quantization
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Solution Overview
Problem
Existing neural video codecs fail to fully exploit spatial and temporal correlations in video frames, resulting in limited rate-distortion (RD) performance, and lack flexibility in adjusting quantization for varying quality and bitrate requirements.
Innovation Solution
Incorporating a hybrid entropy model that utilizes a latent prior for temporal correlation and a dual spatial prior for spatial redundancy, along with a flexible quantization mechanism for dynamic bit allocation, to enhance RD performance and adapt to different content needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional video compression methods are used, then implementation is simpler, but rate-distortion performance is limited
Solution Approach 1:
The entropy model is segmented into multiple independent components: a temporal entropy model using latent priors from previous frames, a spatial entropy model using dual spatial priors from neighboring regions, and a residual entropy model. Each component processes specific types of correlations independently, improving overall RD performance while maintaining manageable complexity through modular design.
Solution Approach 2:
The patent introduces temporal dimension by incorporating latent representations from previous video frames into the entropy modeling process. This extends the traditional 2D spatial entropy modeling to 3D spatiotemporal modeling, allowing the system to exploit temporal correlations and achieve superior RD performance.
2Adaptability or versatility
If fixed quantization is used, then implementation is simpler, but adaptability to different quality and bitrate requirements is reduced
Solution Approach 1:
The quantization mechanism is made dynamic and adaptive through the use of a flexible quantization module that can adjust quantization parameters based on content characteristics, desired bitrate, and quality requirements. The system dynamically selects quantization steps and allocation strategies rather than using fixed quantization, enabling adaptation to varying operational conditions.
Solution Approach 2:
The patent employs variable quantization parameters including spatially-varying quantization matrices, adaptive quantization step sizes, and dynamic bit allocation parameters. These parameters are adjusted based on local content complexity, importance metrics, and rate-distortion optimization to achieve optimal compression performance across different quality levels.
Data Source
AI summary
Innovations in systems, methods, and software for features of a neural image or video codec are described herein. For example, a neural video encoder can receive a current video frame, encode the current video frame to produce encoded data, and output the encoded data as part of a bitstream. As part of the encoding, the encoder can determine a current latent representation for the current video frame, and encode the current latent representation using an entropy model network that includes one or more convolutional layers. As part of the encoding the current latent representation, the encoder can estimate statistical characteristics of a quantized version of the current latent representation based at least in part on a previous latent representation for a previous video frame, and entropy code the quantized version of the current latent representation based at least in part on the estimated statistical characteristics.