Parallel Video Compression Bitstream Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Video data compression systems face a tradeoff between memory bandwidth and hardware cost, requiring expensive memory chips to handle high-quality video, which increases system cost and complexity.
Innovation Solution
A video data compression algorithm with parallel processing capability that maximizes bandwidth resources by encoding input video data into a bitstream, using techniques such as variable length coding, layered coding, and data packing to efficiently store and decode video data, allowing for parallel decoding and improved throughput.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If higher quality video compression is implemented, then video quality is improved, but memory bandwidth requirements increase and system cost increases
Solution Approach 1:
The patent segments the bitstream into multiple partitions, each containing coded data from different video blocks. This segmentation allows parallel processing of multiple partitions simultaneously, increasing throughput and reducing the memory bandwidth required to handle the entire bitstream sequentially. Each partition can be independently processed, enabling efficient utilization of available bandwidth resources.
Solution Approach 2:
The patent introduces a new dimension of parallel processing by organizing the bitstream into multiple partitions that can be processed concurrently. This transforms the traditional single-threaded sequential processing model into a multi-dimensional parallel processing architecture, where multiple partitions are handled in parallel across different processing units or cores, thereby reducing the effective memory bandwidth requirement per processing unit.
2Measurement precision
If higher quality video compression is implemented, then video quality is improved, but hardware cost increases
Solution Approach 1:
By dividing the bitstream into multiple partitions, the patent enables parallel processing across multiple processing units or cores. This segmentation allows the system to achieve high-quality video compression through parallel computation rather than requiring a single complex high-bandwidth memory subsystem, thereby reducing overall hardware cost while maintaining video quality.
Solution Approach 2:
The patent creates multiple copies of processing logic across different partitions that can operate simultaneously. Instead of using expensive high-bandwidth memory hardware, the system replicates processing capabilities across multiple partitions, allowing parallel decomposition and processing of video data, which reduces reliance on expensive memory hardware.
3Productivity
If parallel processing is implemented, then throughput is improved, but device complexity increases
Solution Approach 1:
The patent segments the bitstream into multiple partitions with identifiable boundaries, enabling parallel processing of these partitions. The segmentation includes inserting partition boundary indicators that facilitate synchronized processing across multiple threads or cores. This structured segmentation allows the system to achieve high throughput through parallel processing while managing device complexity through organized data structures and clear partition boundaries.
Data Source
AI summary
Methods and systems for using a video data compression algorithm with parallel processing capability are provided. AC and DC coefficients associated with blocks of the video data, along with quantization errors, may be encoded using a variable length code. The quantization errors may be encoded using a scheme that assigns priorities to the quantization errors based on the position of their associated AC and/or DC coefficients in a block of the video data. The quantization errors may be appended to a bitstream in an order based on these priorities that enables parallel coding of the quantization errors and AC and DC coefficients in each block of video data. Data packing schemes may also be applied to the coded data to maximize the use of bandwidth resources in encoding and/or decoding.