Bit Plane Encoding of Data Arrays for Fixed-Rate Compression
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Solution Overview
Problem
Existing data compression methods for image and texture data in graphics processing systems do not efficiently reduce storage and bandwidth demands, particularly in resource-constrained devices like digital cameras, leading to high demands on processing resources and power consumption.
Innovation Solution
A method involving frequency domain transformation and bit plane encoding, where data is transformed into frequency coefficients, decomposed into bit planes, and encoded in a fixed-size data packet by recursively subdividing bit planes to indicate active sections and coefficients, ensuring a fixed bit rate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data is stored in uncompressed format in frame buffer, then image quality and detail are preserved, but storage capacity and bandwidth consumption increase
Solution Approach 1:
The patent segments image data into multiple bit planes based on significance levels. Each bit plane represents a specific range of magnitude values, allowing selective encoding of important versus less important image details. This segmentation enables the system to preserve critical image quality information while compressing less significant data, directly resolving the contradiction between image quality and storage capacity.
Solution Approach 2:
The patent applies different encoding strategies to different regions of the data based on their significance. High-significance bit planes that contain critical image information are encoded with higher precision, while low-significance bit planes are encoded with lower precision or discarded. This local quality approach ensures that storage capacity is optimized without compromising the overall perceived image quality.
2Quantity of substance
If data is compressed to reduce storage demands, then bandwidth consumption decreases, but processing complexity increases
Solution Approach 1:
The patent performs preliminary sorting and organization of data into bit planes before compression encoding. By pre-organizing the data according to significance levels and magnitude ranges, the system simplifies subsequent encoding operations. This preliminary action reduces the complexity of the main compression process while achieving effective bandwidth reduction.
3Quantity of substance
If compression is applied to reduce memory consumption, then storage requirements decrease, but access time and throughput may be affected
Solution Approach 1:
The patent segments compressed data into organized bit plane structures that facilitate efficient access patterns. This segmentation allows the system to retrieve and process only the necessary bit planes for a given operation, rather than decompressing entire data sets. Consequently, memory consumption is reduced while maintaining high throughput for common operations.
Data Source
AI summary
Disclosed herein is a method of encoding an array of data elements comprising transforming the array from the spatial to the frequency domain, representing the frequency domain coefficients as a plurality of bit plane arrays, and encoding the set of frequency domain coefficients as a data packet having a fixed size by encoding the bit plane arrays in a bit plane sequence working from the bit plane array representing the most significant bit downwards until the data packet is full. Each bit plane array is encoded by recursively subdividing the bit plane array into respective sections and subsections down to the individual coefficients and including in the data packet, so long as there is available space, data indicating the locations of any (sub)sections in that bit plane array that for the first time in the bit plane sequence contain one or more coefficient(s) having a non-zero bit value.