Image Decoding Device Frame Category Reference Management
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Solution Overview
Problem
Conventional image coding methods struggle to achieve correct decoded images and improve coding efficiency when not decoding certain frames, as they lack the ability to manage reference images effectively across multiple frames, leading to incorrect predictions and reduced efficiency.
Innovation Solution
The proposed solution involves classifying frames into categories, allowing for the selection of predicted images from past frames within each category, and encoding reference image specifying data to manage reference images efficiently, enabling correct decoding and improved coding efficiency even when not decoding all frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple decoded images are stored in the reference image memory to improve prediction accuracy, then coding efficiency is improved, but the complexity of managing reference images across multiple frames increases
Solution Approach 1:
The patent segments the reference image memory into multiple independent memory spaces, each dedicated to storing decoded images from specific frame categories. This segmentation allows the system to manage reference images efficiently by isolating different frame types (e.g., P frames, B frames) in separate memory regions, thereby reducing management complexity while maintaining the ability to store multiple reference images for improved prediction accuracy.
2Measurement precision
If reference image specifying data is encoded for each block to enable precise reference selection, then prediction accuracy is improved, but the code amount increases
Solution Approach 1:
The patent applies local quality by encoding reference image specifying data selectively at the block level only when necessary. Instead of uniformly encoding reference information for all blocks, the system encodes reference image specifying data for blocks that require precise reference selection, thereby maintaining prediction accuracy where needed while reducing overall code amount where standard reference images suffice.
3Productivity
If frames are classified into categories to enable selective decoding, then coding efficiency is improved, but the complexity of classifying and managing frames increases
Solution Approach 1:
The patent implements dynamics by creating a flexible frame classification system that can adapt to different decoding requirements. The system dynamically assigns frames to categories based on their properties (e.g., temporal reference, prediction type) and allows the classification structure to be configured according to specific application needs, thereby improving coding efficiency while managing complexity through adaptability rather than rigid fixed structures.
Data Source
AI summary
In order to make it possible to obtain the correct decoded image even in the case of not decoding a particular frame of the encoded data and improve the coding efficiency, the predicted image production unit 103 selects the image data from the image data of a plurality of frames in the reference image memory 107 which are encoded in the past, of the i-th (1≦i≦j) category, for the current frame which is classified as the j-th category by the image classifying unit 102, and produces the predicted image. The difference encoding unit 104 encodes a difference between the image data of the current frame and the predicted image. Also, the current category encoding unit 106 encodes the category number of the current frame, and the reference image specifying data encoding unit 105 encodes the reference image specifying data which specifies the image data selected from the reference image memory 107.