Cloud Streaming Server Changed Region Encoding
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Solution Overview
Problem
Cloud streaming services based on video codecs are inefficient when dealing with static images, as they capture and encode entire screens, leading to increased data usage and resource consumption, and existing still image compression techniques lack adaptability to varying image types and frame changes.
Innovation Solution
A cloud streaming server that determines and encodes only the changed region between frames using a suitable still image compression technique, such as PNG or JPEG, based on image features and resolution, to optimize resource usage and reduce data transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If video codec is used for cloud streaming service, then entire screen can be captured and transmitted, but data usage and resource consumption increase significantly
Solution Approach 1:
The patent divides the screen into multiple regions and identifies changed regions between frames. Only the changed regions are captured and transmitted using still image compression, while unchanged regions are omitted. This segmentation approach reduces data transmission volume and server resource consumption while maintaining service completeness for dynamic content.
2Loss of energy
If still image compression technique is applied to entire screen, then resource usage reduces, but compression efficiency varies significantly depending on image type
Solution Approach 1:
The patent applies different compression techniques to different regions based on their characteristics. Changed regions are identified and processed with appropriate still image compression methods (PNG, JPEG, or hybrid) selected based on image type analysis. This local quality approach ensures optimal compression efficiency for each region while reducing overall server resource usage.
Solution Approach 2:
The patent dynamically selects compression parameters and techniques based on image features such as color depth, complexity, and type. By analyzing image characteristics and adjusting compression parameters accordingly, the system achieves efficient compression across varying image types while minimizing server resource consumption.
3Loss of energy
If changed region detection is performed, then data transmission volume reduces, but detection accuracy is critical for service quality
Solution Approach 1:
The patent implements a dynamic changed region detection mechanism that adapts to different screen content types and change patterns. The detection algorithm dynamically adjusts sensitivity and processing intensity based on frame characteristics, ensuring accurate detection of changed regions while maintaining service quality. This dynamic approach reduces false positives and negatives in changed region identification.
4Productivity
If adaptive encoding technique is implemented, then compression efficiency improves, but system complexity increases
Solution Approach 1:
The patent performs preliminary analysis of image features and changed regions before applying compression techniques. By pre-processing and categorizing content in advance, the system can select appropriate encoding methods without complex real-time decision-making during compression. This preliminary action reduces system complexity while maintaining high compression efficiency through adaptive technique selection.
Data Source
AI summary
The present invention relates to a system for a cloud streaming service, a method for same using a still-image compression technique and an apparatus therefor, particularly the method allowing a still image-based cloud streaming service by comparing the previous frame and current frame to determine and capture the region in the current frame which has changed from the previous frame, and transmitting, to a user, the changed region encoded with the still-image compression technique. By utilizing a still-image compression technique appropriate to the image type when providing the cloud streaming service, the compression efficiency of the still image and the speed of the cloud streaming service can be improved.