Multi-pass Texture Compression for GPU and Network Efficiency
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Solution Overview
Problem
Conventional systems face challenges in transmitting high-fidelity texture images due to limited network capacity and device constraints, leading to suboptimal quality and compatibility issues with graphical processing units (GPUs) and network bandwidth, particularly due to non-random access and inefficient compression methods.
Innovation Solution
The implementation of a multi-pass compression technique that compresses texture images in a GPU-friendly manner using CG1 compression, followed by network-friendly CN compression, and decompression at the client device to generate a tri-pass-compressed texture image, allowing for efficient rendering and processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If texture images are transmitted in uncompressed or lightly compressed format to maintain image quality, then image quality is improved, but network bandwidth consumption increases and delivery speed decreases
Solution Approach 1:
The patent segments the compression process into multiple passes: a first pass that creates an intermediate compressed format, followed by a second pass that delivers the final compressed texture. This segmentation allows optimization at different stages - the first pass prepares data in a GPU-friendly intermediate format, while the second pass delivers network-optimized compressed textures, resolving the contradiction between quality and bandwidth usage.
Solution Approach 2:
The system performs preliminary compression in the first pass to create an intermediate format that is both GPU-friendly and suitable for further compression. This preliminary action prepares the texture data in advance, allowing the second pass to achieve higher compression ratios without sacrificing quality, thereby reducing network bandwidth consumption while maintaining image quality.
2Quantity of substance
If super-compression is applied to reduce network bandwidth consumption, then network efficiency is improved, but GPU compatibility decreases due to slow decompression and computational intensity
Solution Approach 1:
The patent divides compression into two segments: the first pass produces an intermediate format optimized for GPU processing with favorable decompression characteristics, and the second pass applies additional compression for network efficiency. This segmentation ensures that even though the final format is highly compressed, the intermediate representation maintains GPU compatibility and enables fast decompression.
Solution Approach 2:
The intermediate compressed format produced in the first pass acts as an intermediary between the original texture and the final super-compressed format. This intermediary format serves as a bridge that maintains GPU-friendly properties while enabling aggressive compression in the second pass, thus resolving the contradiction between network efficiency and GPU compatibility.
3Quantity of substance
If conventional compression methods are used to reduce file size, then network delivery is improved, but compatibility with client device GPUs decreases and additional conversion hardware/software is required
Solution Approach 1:
The first compression pass produces an intermediate format that serves multiple functions: it is GPU-friendly, suitable for further compression, and maintains random access capabilities. This multi-functional intermediate format eliminates the need for additional conversion hardware or software at the client device, as it can be directly processed by standard GPUs while still allowing for efficient network delivery through the second compression pass.
Solution Approach 2:
The patent changes the compression parameters and format characteristics between passes. The first pass uses parameters optimized for GPU compatibility and random access, while the second pass uses parameters optimized for network delivery. This parameter transformation allows the system to achieve small file sizes without sacrificing GPU compatibility, as the intermediate format preserves essential GPU-friendly properties.
4Quantity of substance
If full texture images are converted to smaller versions to fit memory storage constraints, then device memory compatibility is improved, but image quality downgrades
Solution Approach 1:
The patent segments the size reduction process into two controlled compression passes rather than a single aggressive downscaling operation. This segmentation allows the system to achieve size reduction through compression algorithms that preserve quality better than simple resizing, maintaining image quality while fitting memory constraints through efficient multi-pass compression.
Data Source
AI summary
The present disclosure relates to systems, methods, and non-transitory computer-readable media to enhance texture image delivery and processing at a client device. For example, the disclosed systems can utilize a server-side compression combination that includes, in sequential order, a first compression pass, a decompression pass, and a second compression pass. By applying this compression combination to a texture image at the server-side, the disclosed systems can leverage both GPU-friendly and network-friendly image formats. For example, at a client device, the disclosed system can instruct the client device to execute a combination of decompression-compression passes on a GPU-network-friendly image delivered over a network connection to the client device. In so doing, client device can generate a tri-pass-compressed-texture from a decompressed image comprising texels with color palettes based on previously reduced color palettes from the first compression pass at the server-side, which reduces computational overhead and increases performance speed.