Guided Probability Models for Adaptive Neural Network Compression
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Solution Overview
Problem
Existing video/image compression methods, particularly in neural networks, face inefficiencies in balancing compression rate and quality due to fixed encoding/decoding orders and inflexible multi-scale approaches, leading to suboptimal performance.
Innovation Solution
A guided probability model for neural networks that dynamically determines the encoding/decoding order and number of steps based on content-specific metrics, using a multi-scale approach with a binary mask and neural networks to optimize compressibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If fixed encoding/decoding order is used in neural network compression, then implementation simplicity is maintained, but compression performance deteriorates due to inability to adapt to content-specific characteristics
Solution Approach 1:
The patent implements dynamic processing by determining the processing order of pixels based on their gradient magnitudes and variances. Instead of using a fixed encoding/decoding order, the system adaptively selects processing sequences that prioritize regions with higher importance (larger gradients and variances), thereby improving compression performance while maintaining reasonable computational complexity through content-aware dynamic adjustment
Solution Approach 2:
The patent changes the processing parameters (processing order and number of steps) based on content-specific metrics such as gradient magnitude and variance. By dynamically adjusting these parameters according to the actual image content, the system achieves better compression performance without requiring fundamentally complex architectural changes to the encoding/decoding framework
2Productivity
If multi-scale approach is used to improve compression, then compression rate increases, but computational complexity increases significantly
Solution Approach 1:
The patent segments the image processing into multiple scales, where pixels are processed at different resolution levels. By dividing the processing task across multiple scales and selectively applying processing steps based on content characteristics, the system improves compression rate while avoiding the full computational burden of processing all pixels at all scales, thus managing computational complexity more effectively
3Adaptability or versatility
If content-specific processing is implemented, then compression performance improves, but processing time increases due to additional calculations
Solution Approach 1:
The patent applies partial processing by determining the number of processing steps dynamically based on content characteristics. Instead of applying the maximum number of processing steps to all pixels, the system selectively applies processing steps only where needed (e.g., to pixels with higher gradient magnitudes or variances), thereby improving compression performance while reducing overall processing time through selective partial action
Data Source
AI summary
In example embodiments, an apparatus, a method, and a computer program product are provided. The apparatus comprises at least one processor; and at least one non-transitory memory including computer program code; wherein the at least one memory and the computer program code are configured to, with the at least one processor, cause the apparatus at least to perform determine a processing order of building blocks to encode or decode a media item; and determine a number of processing steps required to encode or decode the media item; wherein the processing order of building blocks and the number of processing steps are determined based on a content of the media item by using a guided probability model based on a neural network.