Non-Square Block Coefficient Grouping for Video Encoding
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Solution Overview
Problem
Current video compression techniques face challenges in efficiently processing next-generation video contents with high spatial resolution, high frame rate, and high dimensionality, requiring significant increases in memory storage, memory access rate, and processing power.
Innovation Solution
The method involves generating a quantized transform block, splitting it into coefficient groups, determining specific scan orders for entropy encoding and decoding, and arranging coefficients based on distance values and increments to optimize encoding and decoding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If video compression techniques process next-generation video contents with high spatial resolution and high frame rate, then processing capability and output quality are improved, but memory storage, memory access rate, and processing power requirements increase significantly
Solution Approach 1:
The transform block is divided into multiple coefficient groups, and each coefficient group is scanned and encoded separately using different scan orders. This segmentation allows the patent to process large transform blocks in smaller units, reducing memory storage requirements while maintaining high processing capability for next-generation video contents
2Ease of manufacture
If transform coefficients are encoded using traditional scan orders, then encoding process is simple, but compression performance is insufficient for non-square blocks
Solution Approach 1:
The patent dynamically selects different scan orders (first scan order and second scan order) based on the shape characteristics of the transform block. For non-square blocks, a specific scan order is chosen to optimize compression performance, making the encoding process adaptive rather than static while maintaining reasonable complexity
3Ease of operation
If a single scan order is used for all coefficient groups, then encoding process is straightforward, but compression efficiency is reduced
Solution Approach 1:
Different scan orders are applied to different coefficient groups based on their local characteristics and positions within the transform block. This local optimization improves compression efficiency by adapting the scan order to the specific pattern of non-zero coefficients in each region, rather than using a uniform scan order across the entire block
Data Source
AI summary
An image decoding method includes splitting a current block into a plurality of coefficient groups based on the current block is the non-square block, wherein the plurality of coefficient groups include a non-square coefficient group, obtaining coefficients corresponding to the plurality of coefficient groups based on a first scan order and a second scan order, wherein the first scan order represents a scan order among coefficients of the non-square coefficient group and wherein the second scan order represents a scan order among the plurality of coefficient groups, and reconstructing the image based on the coefficients.