Error Plane Coding for Image Data Compression
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Solution Overview
Problem
Conventional frame-based data compression techniques face challenges in efficiently utilizing smaller access units and managing variable bitstream sizes, leading to inefficiencies in hardware implementation and power consumption, particularly when accessing specific data in image and video applications.
Innovation Solution
A two-step compression scheme is employed, where each access unit is initially compressed to a target bit budget using lossy data compression, and any remaining bits are utilized through error plane coding to encode residual data, ensuring precise bitstream size management and improved compression efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If frame-based compression techniques are used with standard block sizes (8×8 or 16×16), then compression efficiency is improved, but hardware implementation complexity increases and flexibility decreases
Solution Approach 1:
The patent divides the image into multiple access units of configurable sizes (e.g., 8×1, 16×1, 4×4, or 8×8 blocks), allowing each unit to be processed independently in hardware. This segmentation enables parallel processing while maintaining flexibility in block size selection, resolving the contradiction between compression efficiency and hardware implementation complexity.
2Device complexity
If smaller access units are used to simplify hardware implementation and enable parallel processing, then hardware complexity is reduced, but bit budget utilization becomes inefficient
Solution Approach 1:
The patent dynamically adjusts the compression parameters and processing depth for each access unit based on its characteristics and the remaining bit budget. This dynamic adaptation allows the system to fully utilize the available bit budget while maintaining simple hardware implementation with smaller access units, resolving the contradiction between hardware complexity and bit budget utilization efficiency.
3Productivity
If frame-based compression is used to achieve good compression ratios, then compression efficiency is improved, but memory access bandwidth requirements increase and power consumption rises
Solution Approach 1:
By segmenting the image into smaller access units that can be processed independently, the patent enables selective compression where only necessary units are fully processed. This reduces the overall memory access bandwidth requirement and power consumption while maintaining acceptable compression ratios, resolving the contradiction between compression efficiency and power consumption.
4Productivity
If lossy compression is applied to achieve high compression ratios, then compression efficiency is improved, but data quality deteriorates
Solution Approach 1:
The patent applies different compression strategies to different access units based on their local characteristics. Important regions may receive higher quality treatment while less critical regions use more aggressive compression. This local quality adaptation maintains overall data quality while achieving high compression ratios, resolving the contradiction between compression efficiency and data quality.
Data Source
AI summary
A method and apparatus of image data compression and decompression are disclosed. According to an embodiment of the present invention, the compression method partitions the image data into access units and encodes each access unit into a bitstream according to a target bit budget. Each access unit is encoded using first data compression to generate a first bitstream and the residual data is further encoded using second data compression to generate a second bitstream if the first bitstream is smaller than the target bit budget. In one example, the second data compression comprises bit plane coding applied to bit plane-ordered data, wherein the bit plane-ordered data is generated by scanning from a most significant bit to a least significant bit of the residual data in a bit plane-wise order. The decompression method comprises steps to recover reconstructed data from the first and second bitstreams.