Dynamic Audio-Visual Compression Using GAN Regeneration
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Solution Overview
Problem
The increasing complexity of computer systems has led to network bandwidth and storage space becoming significant limiting factors in capturing and processing high-resolution images and videos, as they require more digital bits, consuming considerable resources and often result in insufficient storage or bandwidth for efficient data transfer and storage.
Innovation Solution
A network bandwidth and storage space-aware method using a generative adversarial network (GAN) to analyze scenes and determine which objects can be redeveloped to a threshold quality level, generating an optimized capture plan to reduce the size of media content by capturing objects at lower resolutions or frame rates, and dynamically adjusting quality levels based on available storage and bandwidth constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution images and videos are captured to improve clarity and quality, then image quality is improved, but network bandwidth and storage space are consumed considerably
Solution Approach 1:
The image is segmented into multiple objects, and each object is encoded at a different resolution based on its importance. Important objects are captured at high resolution while less important objects are captured at lower resolution, reducing overall data size while maintaining perceptual quality.
Solution Approach 2:
Different regions of the image are assigned different quality levels based on local importance. Objects that are more important for the scene understanding are encoded with higher quality, while less important regions use lower quality encoding, optimizing the trade-off between quality and data size.
2Measurement precision
If more digital bits are used to create clearer images and video, then image clarity is improved, but storage space consumption increases
Solution Approach 1:
The image is divided into multiple objects, and each object is independently encoded at a resolution appropriate to its importance. This segmentation allows the system to allocate storage efficiently, using more bits for important objects and fewer bits for less important ones.
Solution Approach 2:
The encoding resolution parameter is dynamically adjusted for each object based on its importance and the available storage space. When storage is limited, the system reduces the resolution parameter for less important objects while maintaining high resolution for important objects.
3Measurement precision
If more digital bits are used to encode video feeds, then video quality is improved, but network bandwidth consumption increases
Solution Approach 1:
The video feed is segmented into multiple objects, and each object is transmitted at a different bitrate based on its importance. This allows the system to optimize network bandwidth usage by allocating more bandwidth to important objects and less to less important ones.
Solution Approach 2:
Different objects in the video are transmitted with different quality levels. Important objects that require detailed analysis are transmitted at high quality, while less important objects are transmitted at lower quality, optimizing the use of network bandwidth.
4Quantity of substance
If objects are captured at lower resolution to reduce data size, then storage and bandwidth usage is reduced, but image quality may deteriorate
Solution Approach 1:
The system applies different quality levels to different objects based on their importance. Critical objects that need high quality for analysis are captured at high resolution, while less important objects are captured at lower resolution, optimizing the balance between data size and quality.
Solution Approach 2:
A GAN-based regeneration module acts as an intermediary to reconstruct high-quality images from lower-resolution inputs. This allows the system to capture objects at lower resolution to reduce data size, then use the GAN to restore quality for objects that need it.
Data Source
AI summary
Disclosed are techniques for dynamic compression of audio-visual data, including a digital encoding method, digital compression method, and a system. One embodiment of the digital encoding method may comprise identifying a scene projected onto a sensor, wherein the scene comprises a plurality of objects. Some embodiments may further comprise analyzing the plurality of objects to determine which of the plurality objects can be redeveloped by a GAN regeneration module to a threshold quality level and which of the plurality of objects cannot be redeveloped with the GAN regeneration module to the threshold quality level. Some embodiments may further comprise generating an optimized capture plan based on the analyzing. Some embodiments may further comprise encoding the scene pursuant to the optimized capture plan to produce a digital media file.