Inter-Channel Entropy Prediction for Faster Image Decoding
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Solution Overview
Problem
Existing image compression methods using deep learning are time-consuming, particularly at the decoding stage, due to the computational complexity of pixel-wise autoregression.
Innovation Solution
Implementing a channel-wise approach for image encoding and decoding using inter-channel prediction of entropy parameters, which reduces the computational complexity by predicting entropy parameters for channels instead of individual pixels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If pixel-wise autoregression is used for image encoding, then encoding precision is improved, but decoding time increases significantly
Solution Approach 1:
The image is divided into multiple channels (e.g., color channels in RGB images), and entropy parameters are predicted for each channel separately based on corresponding parameters from other channels. This segmentation reduces the overall computational complexity while maintaining encoding precision for each individual channel.
Solution Approach 2:
The approach transitions from pixel-wise processing (2D spatial dimension) to channel-wise processing (adding a channel dimension). By predicting entropy parameters in the channel dimension rather than across all pixels, the method reduces computational complexity from O(W×H) to O(C) where C is the number of channels, significantly accelerating decoding while preserving encoding accuracy.
2Measurement precision
If pixel-wise autoregression is used for image decoding, then decoding accuracy is maintained, but computational complexity increases
Solution Approach 1:
The decoding process segments the image into channels, with each channel's entropy parameters being determined based on corresponding parameters from other channels. This segmentation maintains decoding accuracy for each channel while reducing overall computational complexity by avoiding pixel-wise autoregression across the entire image.
Solution Approach 2:
The method changes the dimension of processing from pixel-level (spatial 2D) to channel-level (spectral dimension). By performing entropy parameter prediction across channels rather than across pixels, the computational complexity is dramatically reduced while decoding accuracy is preserved through channel-specific parameter optimization.
3Productivity
If channel-wise encoding with inter-channel prediction is used, then decoding speed is improved, but encoding complexity increases
Solution Approach 1:
The encoding process performs preliminary computation of entropy parameters for reference channels, which are then reused during decoding for predicting parameters in other channels. This preliminary action shifts computational burden to the encoding side, enabling significantly faster decoding speed while managing encoding complexity through intelligent parameter reuse.
Solution Approach 2:
Entropy parameters from decoded channels serve as intermediaries for predicting parameters in other channels. The first decoded channel's entropy parameters act as a mediator to determine parameters for subsequent channels, enabling fast decoding through parameter prediction rather than full recomputation, thus improving decoding speed while controlling encoding complexity.
Data Source
AI summary
Embodiments of the present disclosure relate to methods, devices, apparatuses, and program products for image encoding and decoding. An example method comprises: dividing an image into a plurality of channels; encoding a first channel of the plurality of channels based on entropy encoding; obtaining a first entropy parameter for the first channel; determining a second entropy parameter for a second channel of the plurality of the channels based on the first entropy parameter; and encoding the second channel based on the second entropy parameter. The embodiments of the present disclosure can significantly accelerate decoding of images.