Variable Refresh Rate Display for Cloud Gaming Latency

The patent implements variable refresh rate (VRR) technology that dynamically adjusts the frame rate of displayed content based on real-time detection of user eye movement characteristics. When the user is actively tracking moving objects, the system maintains high frame rates to preserve content quality. When the user's gaze is stable or not actively tracking, the system reduces frame rates to conserve network bandwidth. This dynamic adjustment resolves the contradiction by making frame rate adaptive rather than fixed.

The system changes the temporal parameter (frame rate) of content delivery based on detected user viewing behavior. By monitoring eye movement patterns and determining when users are actively engaged with moving objects versus when their gaze is stable, the system adjusts the frame rate parameter accordingly. This parameter change strategy allows the system to maintain high content quality only when necessary, reducing overall network bandwidth consumption while preserving user experience during critical viewing moments.

The display apparatus dynamically adjusts its refresh rate based on detected user eye movement characteristics rather than maintaining a fixed high refresh rate. When users are actively tracking moving objects, the display maintains high refresh rates to ensure smoothness. When user gaze is stable or not actively tracking, the display reduces refresh rates to lower power consumption and heat generation. This dynamic adaptation resolves the contradiction between display smoothness and energy efficiency.

The system changes the temporal parameter (refresh rate) of the display based on real-time detection of user viewing behavior patterns. By monitoring whether users are actively engaged with moving objects or have stable gaze, the system adjusts the refresh rate parameter dynamically. This allows the display to maintain smooth visual output only when users actually need it, significantly reducing power consumption and heat generation during periods when high smoothness is not required for user experience.

The system implements self-service by automatically detecting user eye movement characteristics and autonomously determining optimal frame rates without requiring manual user input or complex external control systems. The display apparatus and server work together to automatically monitor viewing behavior, analyze eye movement patterns, and adjust content delivery parameters in real-time. This self-service approach achieves high network bandwidth efficiency while avoiding the complexity that would arise from manual configuration or complex external control systems.

The system employs feedback mechanisms where the display apparatus detects user eye movement characteristics and feeds this information back to the server, which then adjusts the frame rate of transmitted content accordingly. This closed-loop feedback system enables automatic optimization of network bandwidth usage based on actual user engagement with the content. The feedback mechanism achieves high bandwidth efficiency while keeping system complexity manageable by using straightforward detection and adjustment logic rather than complex algorithms.

The system changes detection parameters based on content characteristics and user behavior patterns. Rather than requiring uniformly high detection precision at all times, the system adjusts detection sensitivity and sampling rates according to the current viewing context. When users are actively tracking moving objects, the system uses higher precision detection. When gaze is stable or content is less dynamic, detection precision requirements are reduced. This parameter adjustment in detection itself resolves the contradiction between power efficiency and measurement precision.

The system applies different detection precision levels to different regions of interest within the content and different time periods based on user engagement patterns. Rather than uniformly high precision detection across the entire field of view and all time periods, the system focuses detection resources on relevant regions where users are actively engaged with moving objects. This local quality approach achieves adequate detection precision where needed while reducing overall power consumption, resolving the contradiction between detection accuracy and energy efficiency.