Motion Vector Candidate List Adjustment for Image Decoding
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Solution Overview
Problem
Current image/video encoding and decoding technologies face challenges in reducing computation complexity while enhancing encoding efficiency, particularly in motion vector prediction and inter prediction methods.
Innovation Solution
The proposed solution involves adjusting the motion vector candidate list by adding or removing specific motion vector candidates based on the maximum number of candidates, determining a prediction motion vector from the adjusted list, and using this approach in both image encoding and decoding methods to reduce computation complexity and improve efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If motion vector candidate list is adjusted by adding or removing candidates, then encoding efficiency is improved, but computation complexity increases
Solution Approach 1:
The patent changes the parameter of motion vector candidate list size by adjusting the maximum number of candidates based on picture type (e.g., setting to 4 for B-frames, 2 for P-frames). This parameter adjustment improves encoding efficiency by providing more candidates when beneficial while controlling complexity by limiting candidates in other cases.
Solution Approach 2:
The patent dynamically adjusts the motion vector candidate list configuration based on picture type and encoding conditions. The maximum number of candidates is not fixed but varies according to the frame type (B-frame, P-frame, I-frame) and other factors, allowing the system to adapt between efficiency and complexity requirements.
2Measurement precision
If more motion vector candidates are considered, then prediction accuracy is improved, but computation complexity increases
Solution Approach 1:
The patent uses parameter changes by setting different maximum numbers of motion vector candidates based on picture type. For B-frames where bidirectional prediction is used, the maximum is set to 4 to improve prediction accuracy, while for P-frames it is set to 2 to balance accuracy with complexity.
Solution Approach 2:
The patent applies local quality by providing different levels of motion vector candidate consideration for different picture types. B-frames receive more comprehensive candidate evaluation (up to 4 candidates) while P-frames receive streamlined evaluation (up to 2 candidates), matching the local requirements of each frame type.
Data Source
AI summary
A method for decoding an image according to the present invention comprises the steps of: decoding a residual block by quantizing and inverse transforming an entropy-decoded residual block; generating a prediction block via motion compensation; and decoding an image by adding the decoded residual block to the prediction block, wherein on the basis of the maximum number of motion vector candidates of the motion vector candidate list related to the prediction block, a motion vector candidate list is adjusted by adding a particular motion vector candidate or by discarding a portion from among the motion vector candidates, and in the prediction block generation step, a prediction motion vector of the prediction block is determined on the basis of the adjusted motion vector candidate list. Accordingly, the complexity of arithmetic operations is reduced during encoding/decoding of an image.