Multi-Pass Sample Adaptive Offset Filtering for Video Coding
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Solution Overview
Problem
Current video coding techniques face limitations in achieving optimal compression efficiency and perceived video quality due to the sequential nature of sample adaptive offset (SAO) filtering, which introduces latency and requires significant buffer sizes.
Innovation Solution
Implementing multi-pass SAO filtering, where two or more SAO filtering passes are performed either sequentially or concurrently, allowing for improved compression efficiency and reduced latency by applying offset values based on pixel classifications and bands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If sequential multi-pass SAO filtering is performed, then compression efficiency and video quality are improved, but processing time and latency increase
Solution Approach 1:
The video processing pipeline is segmented into multiple independent SAO filtering passes, each handling specific pixel classifications or bands. These passes are divided into parallel execution units that can operate simultaneously on different data blocks, allowing compression efficiency improvements while reducing overall processing time through concurrent execution.
Solution Approach 2:
The patent transitions from sequential single-pass SAO filtering to multi-dimensional parallel processing by introducing multiple passes that operate concurrently on different aspects of video data (different pixel classifications, bands, or processing stages). This dimensional expansion allows simultaneous execution of multiple filtering operations, improving both compression efficiency and reducing latency.
2Manufacturing precision
If sequential multi-pass SAO filtering is performed, then video quality is improved, but buffer size requirements increase
Solution Approach 1:
The video data processing is segmented into multiple passes where each pass handles specific filtering operations. By dividing the overall SAO filtering into discrete, manageable passes with defined input/output interfaces, the patent reduces the intermediate buffer size requirements compared to holding all data for complete multi-pass processing before output.
Solution Approach 2:
The patent performs preliminary SAO filtering operations in earlier passes, producing intermediate results that are sufficiently refined for subsequent passes. This preliminary action allows later passes to work with pre-processed data, reducing the amount of data that needs to be buffered and re-processed, thereby reducing overall buffer size requirements.
3Loss of time
If parallel multi-pass SAO filtering is implemented, then latency is reduced, but device complexity increases
Solution Approach 1:
The complex SAO filtering process is segmented into multiple standardized passes with well-defined interfaces and operations. Each pass is a self-contained processing unit with clear input/output specifications, making the overall complex system manageable through modular design. This segmentation allows parallel implementation while controlling device complexity through systematic organization.
Solution Approach 2:
The patent implements universal SAO filtering pass structures that can handle multiple types of pixel classifications and bands using the same basic processing framework. This multi-functionality reduces device complexity by avoiding the need for separate specialized processing units for each filtering scenario, while still enabling parallel execution of multiple passes.
Data Source
AI summary
In general, techniques are described for performing multiple passes of sample adaptive offset (SAO) filtering when coding video data. A video decoding device comprising one or more processors may perform the techniques. The processors may determine a first SAO pixel classification for a block of video data and determine a first offset value based on the first SAO pixel classification and one or more pixel values of the block. The one or more processors may also determine a second SAO pixel classification for the block and determine a second offset value based on the second SAO pixel classification and the one or more pixel values of block of video data. The processors may then apply the first offset value and the second offset value to the block of video data to generate a block of SAO filtered video data.