Feature Tensor Compression via Inactive Channel Removal and Frame Packing
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Solution Overview
Problem
Existing video coding systems face inefficiencies in compressing feature tensors due to the inclusion of inactive channels, which consume bandwidth without contributing to the video data.
Innovation Solution
Implement range-based channel removal and frame packing techniques in video codecs, where inactive channels are identified and either removed, scaled down, truncated, or filled with mean values, while active channels are repacked for efficient encoding and decoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all channels are transmitted in feature tensors, then complete information is preserved, but bandwidth usage increases due to inactive channels
Solution Approach 1:
The patent extracts and removes inactive channels from feature tensors before transmission. The encoder identifies channels with ranges below a threshold value as inactive and removes them from the transmitted data stream, while the decoder reconstructs these channels using stored mean values, thereby reducing bandwidth usage without losing essential information.
Solution Approach 2:
The patent discards inactive channels during encoding by removing them from the feature tensor transmission, and recovers them during decoding by generating replacement channels with mean values. This process eliminates wasteful transmission of redundant data while maintaining information completeness.
2Productivity
If channel reduction is performed to reduce bandwidth, then transmission efficiency improves, but information completeness may be compromised
Solution Approach 1:
The patent employs feedback mechanisms where the encoder sends channel activation indications to the decoder, and the decoder uses these indications along with stored mean values to accurately reconstruct inactive channels. This feedback loop ensures that channel reduction does not compromise information completeness.
Solution Approach 2:
The patent performs preliminary actions by pre-calculating and storing mean values for each channel in the feature tensor. When channels are reduced during transmission, the decoder can immediately reconstruct them using these pre-computed mean values, ensuring information completeness is maintained without compromising transmission efficiency.
3Loss of energy
If inactive channels are removed to enhance compression, then compression efficiency improves, but decoding complexity increases
Solution Approach 1:
The patent implements self-service decoding where the decoder autonomously reconstructs inactive channels using stored mean values and channel activation indications received from the encoder. This self-service mechanism simplifies the overall system by eliminating the need for complex coordination between encoder and decoder, thereby improving compression efficiency without significantly increasing decoding complexity.
Data Source
AI summary
Systems, methods, and instrumentalities are disclosed herein for range-based channel removal and frame packing for compressing feature tensors with video codecs. In examples, the encoder may determine whether a channel is active or inactive. The encoder may perform channel reduction based on the determination that the channel is inactive. A feature tensor may be encoded based on the reduced channel. In examples, the decoder may obtain a channel activation indication associated with a channel. Based on the channel activation indication associated with the channel, the decoder may determine whether the channel is active or inactive. A feature tensor may be decoded based on the determination of whether the channel is active or inactive.