Compression Level Transition in Streaming Image Systems
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Solution Overview
Problem
Interactive streaming image systems face challenges in managing compression levels efficiently, as existing methods like MPEG and motion-JPEG are not optimal for interactive applications that require frame-by-frame customization, leading to resource-intensive and bandwidth-consuming processes.
Innovation Solution
A system and method for transitioning compression levels in a streaming image system, where a smoothing module generates a multi-frame smoothing routine based on an initial and target compression level, and the number of frames, to dynamically adjust compression levels for each image frame, allowing for efficient bandwidth use and improved user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If MPEG compression is used for interactive streaming images, then compression efficiency is improved, but adaptability to frame-by-frame customization deteriorates
Solution Approach 1:
The system dynamically switches between different compression algorithms (MPEG for bulk data, motion-JPEG for interactive frames) based on real-time user interaction detection. This allows the system to adapt compression behavior to match the specific requirements of each frame type, resolving the contradiction between compression efficiency and adaptability.
Solution Approach 2:
Different compression methods are applied to different portions of the image stream locally - MPEG compression for predictable content and motion-JPEG for interactive content. This localized approach allows each region to be optimized for its specific characteristics, achieving both compression efficiency and adaptability.
2Adaptability or versatility
If motion-JPEG compression is used for interactive streaming images, then adaptability to frame-by-frame customization is improved, but network resource consumption increases
Solution Approach 1:
The system dynamically selects compression algorithms based on user interaction detection. For interactive frames, motion-JPEG is applied to maintain adaptability. For non-interactive frames, MPEG is used to reduce network resource consumption. This dynamic selection resolves the contradiction between adaptability and resource consumption.
Solution Approach 2:
Instead of applying the more resource-intensive motion-JPEG compression to all frames, the system applies it only partially to interactive frames where it is necessary. This partial application reduces overall network resource consumption while maintaining adaptability where needed.
3Quantity of substance
If compression level is increased to reduce bandwidth, then network traffic is decreased, but image quality deteriorates
Solution Approach 1:
The system dynamically adjusts compression levels based on user interaction detection and network conditions. During interactive sessions, lower compression is applied to maintain image quality. During non-interactive periods, higher compression is applied to reduce bandwidth. This dynamic adjustment resolves the contradiction between network traffic reduction and image quality preservation.
Solution Approach 2:
The system changes compression parameters (compression level, algorithm type) based on real-time conditions. By varying these parameters dynamically, the system can optimize the balance between network traffic and image quality for different operational contexts.
4Manufacturing precision
If compression level is decreased to improve image quality, then network traffic increases, but bandwidth consumption increases
Solution Approach 1:
The system dynamically adjusts compression levels based on user interaction detection. When interaction is detected, compression is reduced to improve image quality. When no interaction occurs, compression is increased to reduce network traffic. This dynamic adaptation resolves the contradiction between image quality and bandwidth consumption.
Solution Approach 2:
The system uses user interaction feedback to determine appropriate compression levels. Real-time detection of user interaction provides feedback that triggers appropriate compression behavior, allowing the system to optimize the balance between image quality and network traffic based on actual usage patterns.
Data Source
AI summary
Methods for transitioning compression levels of a streaming image system are disclosed. One embodiment provides a method for transitioning compression levels between image frames in a streaming image system. Embodiments may include receiving by a smoothing module a request for a new compression level associated with an image frame. Embodiments may also generally include performing by the smoothing module a smoothing heuristic by generating a multi-frame smoothing routine based on an initial compression level, a target compression level, and a number of frames to achieve the target compression level. Embodiments may also include setting by the smoothing module the new compression level for the image frame based on the generated multi-frame smoothing routine. Further embodiments may include transmitting by the smoothing module an indication of the new compression level to a content encoder.