Wavelet Image Compression for Low-Latency Wireless Streaming
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Solution Overview
Problem
Current image compression schemes are not suitable for wireless streaming of high-resolution images between two wireless devices in a high-performance, low-latency, and energy efficient manner, affecting user experience and accuracy of image reproduction.
Innovation Solution
A wavelet-based image compression scheme that generates wavelet coefficients using discrete wavelet transform, applies entropy coding to remove bits, serializes coefficients, and reconstructs images using inverse discrete wavelet transform in a single pass, enabling high-performance and low-latency wireless streaming without dedicated hardware blocks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If current image compression schemes are used for wireless streaming, then basic image transmission is possible, but performance is insufficient and latency is high
Solution Approach 1:
The image is divided into multiple tiles that can be processed and transmitted independently in parallel. Each tile undergoes wavelet transform and entropy coding separately, allowing simultaneous processing of different regions. This segmentation enables the system to achieve high throughput by utilizing multiple processing units concurrently, directly improving wireless streaming performance while reducing overall latency through parallel execution.
Solution Approach 2:
The wavelet transform and entropy coding are performed on image tiles before transmission, preparing the data in an optimized compression format in advance. The encoder pre-processes the image data into wavelet coefficients and applies entropy coding to remove redundant bits, so that the transmitted bitstream requires minimal processing at the decoder side, thereby reducing transmission time and latency.
2Manufacturing precision
If high-resolution images are compressed and transmitted, then image quality is maintained, but energy consumption increases
Solution Approach 1:
The entropy coding process extracts and removes redundant bits from the wavelet coefficients, transmitting only the essential information needed to reconstruct high-resolution images. By identifying and eliminating statistically redundant data patterns in the wavelet domain, the system achieves efficient compression that maintains image quality while significantly reducing the number of bits that need to be transmitted and processed, thereby lowering energy consumption.
3Loss of information
If complex compression algorithms are used, then compression ratio is improved, but computational complexity increases
Solution Approach 1:
The patent replaces complex sequential mechanical processing with a parallel processing architecture where multiple tiles are processed simultaneously using wavelet transforms. The wavelet-based compression algorithm substitutes traditional multi-pass mechanical compression approaches with a single-pass parallel implementation that achieves comparable or better compression ratios while reducing computational complexity through efficient parallel execution and localized processing of image tiles.
Data Source
AI summary
This disclosure provides systems, devices, apparatus, and methods, including computer programs encoded on storage media, for a high-performance and low-latency implementation of a wavelet-based image compression scheme. A processor may generate, in a single pass for each of a plurality of horizontal and vertical divisions of an image frame, a set of wavelet coefficients for the image frame based on a DWT. The processor may select a set of bits associated with the set of wavelet coefficients to remove based on an entropy coding process. The processor may serialize the set of wavelet coefficients, where the set of serialized wavelet coefficients do not include the set of bits. The processor may output, for a second device, a bitstream including the set of serialized wavelet coefficients.