Image encoding / decoding method and recording medium for said method

By combining multi-directional prediction and a motion vector candidate list, the problem of low encoding/decoding efficiency for high-resolution and high-quality images is solved, achieving more efficient image data compression.

CN116567263BActive Publication Date: 2026-06-30ELECTRONICS & TELECOMM RES INST
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
ELECTRONICS & TELECOMM RES INST
Filing Date
2017-05-23
Publication Date
2026-06-30

AI Technical Summary

Technical Problem

Existing technologies have limited efficiency in encoding/decoding high-resolution and high-quality images, especially in motion compensation, where the use of only unidirectional and bidirectional prediction makes it difficult to meet the requirements for efficient compression.

Method used

A multi-directional prediction method is employed, including unidirectional, bidirectional, tridirectional, and quadridirectional predictions. By generating multiple candidate lists of motion vectors, combining spatial, temporal, and predefined motion vector candidates, a weighted sum is used to determine the final prediction block.

Benefits of technology

It improves image encoding/decoding efficiency and optimizes data compression performance for high-resolution and high-quality images by combining motion compensation of motion vector candidates.

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Abstract

This invention relates to an image encoding / decoding method and a recording medium for the method. To this end, a method for decoding an image may include the following steps: generating a list of multiple motion vector candidates based on the inter-frame prediction direction of a current block; obtaining multiple motion vectors for the current block using the list of multiple motion vector candidates; determining multiple prediction blocks for the current block using the multiple motion vectors; and obtaining a final prediction block for the current block based on the multiple prediction blocks.
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