Video Resolution Enhancement via Bias Sampling
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Solution Overview
Problem
Conventional video transmission over computer networks often occurs at lower resolutions than captured due to bandwidth limitations, resulting in reduced quality of shared videos.
Innovation Solution
Introducing a bias into low-resolution video frames by sampling high-resolution frames, using techniques like pseudo-random or scanning biases, and transmitting these biased frames with a bias series to enable higher resolution image data reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If video is transmitted at lower resolution to conserve bandwidth, then bandwidth consumption is reduced, but image quality deteriorates
Solution Approach 1:
The video transmission is segmented into multiple low-resolution frames, each containing a bias sample from high-resolution data. Instead of transmitting one complete high-resolution video, the system divides the high-resolution information into distributed bias samples across multiple frames, which are then reconstructed at the receiving end to recover high-resolution quality.
Solution Approach 2:
A bias sample acts as an intermediary between high-resolution source data and low-resolution transmitted frames. The bias contains high-resolution information that mediates the transformation from low-resolution transmission to high-resolution reconstruction, enabling quality recovery without direct high-resolution transmission.
2Manufacturing precision
If multiple low-resolution frames with bias samples are transmitted and combined, then effective resolution increases, but processing complexity increases
Solution Approach 1:
The bias sample is pre-computed from high-resolution frames before transmission. This preliminary action embeds the essential high-resolution information into the low-resolution frames in advance, so that the receiving end only needs to perform straightforward combination operations rather than complex high-resolution processing.
Solution Approach 2:
Instead of transmitting complete high-resolution frames, the system creates simplified copies (bias samples) that contain the essential high-resolution information. These bias copies are embedded in low-resolution frames and later combined to reconstruct the full high-resolution image, reducing transmission and processing loads.
3Manufacturing precision
If bias sampling is applied to all frames, then resolution improvement is maximized, but data transmission volume increases
Solution Approach 1:
Instead of uniformly applying bias sampling to all frames, the system selectively applies bias sampling based on local needs. Frames are evaluated to determine whether they contain sufficient high-resolution information worth transmitting, allowing the system to concentrate bandwidth on frames that provide the most value for resolution improvement.
Solution Approach 2:
The system applies bias sampling partially rather than universally - only to frames where it provides meaningful resolution improvement. This partial action avoids the overhead of processing and transmitting bias samples for every frame, optimizing the balance between resolution improvement and data volume.
Data Source
AI summary
Systems, methods, and non-transitory computer readable media can obtain a first plurality of frames of a video having a first resolution. For each frame of the first plurality of frames, a portion of a corresponding frame of a version of the video having a second resolution that is higher than the first resolution can be sampled to generate a bias for the frame. A second plurality of frames of the video including the respective bias can be generated, wherein the second plurality of frames corresponds to the first plurality of frames. A second plurality of frames of the video including the respective bias can be generated.