Environment-Based Lossy Compression for Efficient Content Rendering
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Solution Overview
Problem
Conventional compression techniques are inefficient and inaccurate due to reliance on predefined data and rough estimates based on a defined reference environment, leading to suboptimal performance in real-world scenarios.
Innovation Solution
A novel technique for real-time data compression that utilizes real-time sensory input, such as eye tracking and environmental monitoring, to dynamically adjust compression levels based on user behavior and environment, allowing for lossy compression that is imperceptible to the user, particularly by compressing peripheral image data and adjusting resolution based on user distance from the display.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional compression techniques are used, then data compression is achieved, but compression efficiency and accuracy are insufficient due to reliance on predefined data and rough estimates
Solution Approach 1:
The patent applies dynamics by transitioning from static, predefined compression parameters to dynamic, real-time adjustment of compression levels. The system continuously monitors user behavior (eye tracking data), environmental conditions (lighting, distance), and device state to adaptively modify compression parameters during operation. This enables the compression algorithm to optimize between efficiency and accuracy based on current conditions rather than relying on fixed reference environments.
Solution Approach 2:
The patent implements feedback mechanisms by utilizing real-time sensory input from eye tracking cameras, environmental sensors, and user interaction data. This feedback loop allows the system to continuously assess actual viewing conditions and adjust compression parameters accordingly. The feedback from multiple sensors creates a closed-loop system that refines compression accuracy while maintaining efficiency, directly addressing the limitation of conventional techniques that lack real-time condition assessment.
2Measurement precision
If real-time sensory input is used for dynamic compression adjustment, then compression accuracy improves, but device complexity increases due to additional sensors and processing requirements
Solution Approach 1:
The patent applies universality by designing a multi-functional integrated system where the display device performs multiple functions: it serves as both the display output and incorporates eye tracking cameras, environmental sensors, and processing capabilities within the same device architecture. This multi-functionality reduces the need for separate dedicated components, thereby managing complexity while enabling real-time sensory input collection and processing for accurate dynamic compression.
Solution Approach 2:
The system applies self-service by utilizing the display device's own existing resources (processing power, existing camera modules, sensor data) to perform compression optimization without requiring entirely external systems. The device leverages its inherent capabilities to collect sensory data, process it through machine learning models, and adjust compression parameters autonomously, reducing dependency on external complex infrastructure.
3Use of energy by moving object
If lossy compression is applied to peripheral image data, then data traffic and power consumption are reduced, but image quality may deteriorate
Solution Approach 1:
The patent applies local quality by implementing spatially varying compression strategies where different regions of the image receive different compression levels. Based on eye tracking data that identifies the user's focal point, the system applies high-quality (low compression) rendering to the foveal region where the user is looking, while applying aggressive lossy compression to peripheral regions where the user's visual acuity is lower. This localized approach maintains perceived image quality while significantly reducing overall data traffic and power consumption.
Data Source
AI summary
A mechanism is described for facilitating environment-based lossy compression of data for efficient rendering of contents at computing devices. A method of embodiments, as described herein, includes collecting, in real time, sensory input data relating to characteristics of at least one of a user and a surrounding environment. The method may further include evaluating the sensory input data to mark one or more data portions of data relating to contents, where the one or more data portions are determined to be suitable for compression based on the sensory input data. The method may further include dynamically performing, in real time, the compression of the one or more data portions, where the compression triggers loss of one or more content portions of the contents corresponding to the one or more data portions of the data. The method may further include rendering the contents to be displayed missing the one or more content portions, where the missing of the one or more content portions from the contents is not apparent to the user viewing the contents via a display device.