Adaptive Image Encoding Configuration Optimization
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Solution Overview
Problem
Existing image encoding techniques often result in subpar tradeoffs between file size and visual quality due to using a single encoder configuration for multiple images of varying complexity, leading to inefficient use of storage resources and increased bandwidth requirements.
Innovation Solution
A computer-implemented method that analyzes encoding configurations by using a binary search to optimize the number of bits for each image to achieve a target quality score, ensuring complex images meet quality standards while minimizing unnecessary bits used for simple images, thereby reducing storage and bandwidth needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single encoder configuration is used for multiple source images, then the encoding process is simple and fast, but the tradeoffs between file size and visual quality level become subpar for images with varying complexity
Solution Approach 1:
The patent applies dynamics by making the encoder configuration adaptive rather than static. The system dynamically adjusts encoding parameters based on the complexity characteristics of each source image, allowing the encoder to optimize for both speed and quality according to the specific image being processed. This resolves the contradiction by enabling the encoder to be simple for straightforward images while providing enhanced quality control for complex images.
Solution Approach 2:
The patent implements parameter changes by modifying encoder configuration parameters based on image complexity analysis. The system analyzes source images to determine their complexity level and then adjusts encoding parameters accordingly, such as bitrate, compression level, or quality thresholds. This allows the encoding process to maintain high speed for simple images while achieving superior visual quality for complex images, thus resolving the contradiction between encoding speed and visual quality.
2Manufacturing precision
If more bits are used to encode source images to ensure acceptable visual quality, then visual quality level improves, but storage resources and bandwidth are wasted for simple images
Solution Approach 1:
The patent applies local quality by tailoring the encoding quality to the specific characteristics of each source image rather than applying a uniform quality level to all images. The system analyzes image complexity and allocates encoding resources locally - using higher bitrates and more sophisticated encoding for complex images that require it, and lower bitrates for simple images where high quality is unnecessary. This resolves the contradiction by ensuring visual quality is optimized only where needed, eliminating wasteful bandwidth usage for simple images.
3Loss of energy
If fewer bits are used to encode source images to reduce storage and bandwidth, then bandwidth requirements decrease, but visual quality level becomes unacceptable for complex images
Solution Approach 1:
The patent uses parameter changes to dynamically adjust the relationship between bitrate and visual quality. By analyzing image complexity and modifying encoder parameters accordingly, the system ensures that complex images receive sufficient bits to maintain acceptable visual quality, while simple images use fewer bits without quality degradation. This resolves the contradiction by intelligently allocating bandwidth resources based on actual quality requirements rather than applying a uniform approach.
Data Source
AI summary
In various embodiments, a codec comparison application independently encodes each source image included in a set of source images using a first encoding configuration to generate a first set of encoded images. The codec comparison application also independently encodes each source image included in the set of source images using a second encoding configuration to generate a second set of encoded images. For each encoded image in the first set of encoded images and each encoded image in the second set of encoded images, a visual quality score for a reconstructed source image derived from the encoded image falls within a tolerance of a target visual quality score. Subsequently, the codec comparison application computes a bitrate change based on a first total file size for the first set of encoded image and a second total file size for the second set of encoded images