Subblock Video Reconstruction with Transform-Aware Boundary Filtering
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Solution Overview
Problem
The increasing volume of video data due to higher video size, resolution, and frame rate necessitates improved encoding and decoding efficiency beyond existing compression techniques like H.264/AVC and HEVC.
Innovation Solution
Reconstructing video blocks in units of sub-blocks, involving decoding transform coefficients, deriving residual blocks, and filtering boundaries, with a video decoding apparatus that includes a decoder, inverse transformer, and filtering unit to enhance transformation, quantization, and coefficient encoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If video data is compressed using existing techniques like H.264/AVC or HEVC, then compression efficiency is improved, but encoding and decoding complexity increases with higher video size, resolution, and frame rate
Solution Approach 1:
The current block is divided into multiple sub-blocks, and transformation is performed separately on each sub-block. This segmentation allows the encoding process to handle local variations more efficiently, improving compression performance while managing complexity through localized processing rather than treating the entire block uniformly.
Solution Approach 2:
Different transformation processes are applied to different sub-blocks based on their local characteristics. The filtering process also applies different strengths to boundary regions versus interior regions, achieving local optimization that improves overall compression efficiency without uniformly increasing complexity across the entire block.
2Device complexity
If transformation and filtering are performed on entire video blocks, then processing simplicity is maintained, but compression performance deteriorates due to inability to capture local variations
Solution Approach 1:
The video block is segmented into sub-blocks, allowing transformation to capture local variations in each sub-block. This segmentation improves compression performance by adapting to local frequency characteristics while maintaining relatively simple processing through standardized sub-block transformation procedures.
Solution Approach 2:
The transformation parameters are changed at the sub-block level rather than the entire block level, allowing adaptation to local variations. This enables better compression performance by matching transformation characteristics to local content properties while keeping the overall process manageable through parameter variation at a finer granularity.
3Loss of energy
If sub-block based reconstruction is implemented, then compression performance is improved, but device complexity and processing overhead increase
Solution Approach 1:
The reconstruction process is segmented into sub-block level operations, where transformation and filtering are applied to smaller units. This segmentation improves compression performance by capturing local variations more accurately while managing device complexity through modular, reusable processing blocks that can be efficiently implemented in hardware or software.
Solution Approach 2:
The filtering process applies local quality control by differentiating between boundary regions and interior regions of sub-blocks. Boundary regions receive filtering to reduce artifacts, while interior regions maintain higher fidelity. This local differentiation improves compression performance without uniformly increasing processing complexity across the entire block.
4Reliability
If filtering is applied to all boundaries in reconstructed blocks, then artifact reduction is improved, but processing time and complexity increase
Solution Approach 1:
Filtering is applied selectively based on local needs - boundary regions of sub-blocks receive filtering to reduce blocking artifacts, while interior regions are processed more efficiently. This local quality approach improves artifact reduction where necessary while minimizing processing time in regions that require less intervention.
Solution Approach 2:
Instead of applying full filtering to all regions, the process applies partial filtering action only where boundary artifacts are likely to occur (at sub-block boundaries). This partial action approach achieves sufficient artifact reduction while significantly reducing processing time compared to uniform filtering of the entire block.
Data Source
AI summary
A method is disclosed for reconstructing a current block in units of sub-blocks included in the current block. The method includes: decoding transform coefficients in a transform block of the current block, sub-block information indicating a division form of the sub-blocks and transform type information indicating a transform type applied to the transform block from a bitstream; deriving a residual block of the current block from the transform coefficients on the basis of the transform type indicated by the transform type information and the sub-block information; and filtering boundaries of the sub-blocks in a reconstructed block of the current block derived on the basis of the residual block.