Attention-Based Latent Tensor Context for Video Compression
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Solution Overview
Problem
Existing video compression technologies face challenges in achieving high compression ratios with minimal quality sacrifice, particularly due to limitations in entropy estimation methods that fail to consider spatial and cross-channel correlations, leading to inefficient use of network resources.
Innovation Solution
The proposed method involves separating latent tensors into segments and using an attention layer within a neural network to adaptively weight the importance of previously coded segments, focusing on spatial and cross-channel correlations for improved entropy estimation and encoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional entropy estimation methods are used, then the encoding process is simple, but the compression ratio is insufficient and picture quality deteriorates
Solution Approach 1:
The latent tensor is divided into multiple segments along the spatial and/or channel dimensions. Each segment is processed independently by the neural network to generate context information, which is then aggregated to form the probability model. This segmentation enables the system to capture local correlations effectively while managing computational complexity through modular processing.
Solution Approach 2:
The patent processes segments in different dimensions (spatial and channel dimensions) to capture correlations that traditional methods miss. By transforming the problem from a single-dimension processing approach to multi-dimensional segment processing, the system achieves better entropy estimation without proportionally increasing complexity.
2Loss of information
If deep learning methods are applied to improve compression ratio, then picture quality improves, but computational complexity and resource consumption increase significantly
Solution Approach 1:
By segmenting the latent tensor, the system processes smaller subsets of data through the neural network rather than the entire tensor at once. This reduces the computational burden per processing step while maintaining the benefits of deep learning for entropy estimation, thereby improving compression efficiency with manageable resource consumption.
Solution Approach 2:
The system applies neural network processing selectively to segments that contribute most to entropy estimation accuracy. Not all segments require equal processing depth, allowing the system to achieve good compression ratios without uniformly applying high computational cost across the entire data structure.
3Productivity
If spatial and cross-channel correlations are considered in entropy estimation, then compression performance improves, but the complexity of the probability model increases
Solution Approach 1:
The probability model is constructed by aggregating context information from multiple segmented processing paths. Each segment captures specific spatial or channel correlations, and their combination creates a comprehensive probability model that leverages multi-dimensional correlations without requiring a single overly complex model structure.
Data Source
AI summary
Methods and apparatuses are described for entropy encoding and decoding of a latent tensor, which includes separating the latent tensor into segments in the spatial dimensions, each segment including at least one latent tensor element. An arrangement of the segments is processed by a neural network; the neural network includes at least one attention layer. Based on the processed segment a probability model is obtained for entropy encoding or decoding of a latent tensor element.