Texture Image Tile Sequencing for Video Delivery
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Solution Overview
Problem
The delivery of high-quality 3D media content is hindered by the large amount of data required for 3D scenes, particularly due to the extensive texture data associated with each 3D object, which poses challenges for network and device constraints.
Innovation Solution
The proposed solution involves encoding and delivering texture images as a video sequence, where texture images are divided into tiles of uniform size and sorted into a sequence based on similarity or sub-sequence ordering, allowing for efficient compression and delivery using existing video encoding and streaming infrastructures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If texture images are delivered as individual high-resolution images, then image quality is maintained, but data transmission burden increases significantly
Solution Approach 1:
Multiple texture images are merged into a single video sequence and encoded together, allowing the video encoder to exploit temporal similarities between consecutive frames. This combining approach enables efficient compression while maintaining image quality, as the video encoder can reuse information across frames rather than encoding each texture image independently.
Solution Approach 2:
The delivery format parameter is changed from individual image files to a video sequence. By transforming the data structure from static images to a temporal sequence that can be decoded frame-by-frame, the system achieves better compression efficiency while preserving the original image quality through proper decoding and rendering.
2Loss of energy
If texture images are encoded individually, then compression is applied to each image, but delivery efficiency and adaptability are reduced
Solution Approach 1:
The system merges multiple texture images into a single video sequence that is encoded once by the video encoder. This approach is more efficient than individually encoding each image, as the video encoder can leverage temporal redundancy and similarities between consecutive frames, achieving better compression efficiency and faster delivery.
Solution Approach 2:
The video sequence serves multiple functions: it delivers multiple texture images, enables adaptive bitrate streaming, and allows for efficient compression. The single video encoding process replaces multiple individual image encoding operations, providing a universal solution that handles multiple textures simultaneously with improved efficiency.
3Measurement precision
If high-resolution texture data is delivered, then 3D content quality is maintained, but network bandwidth requirements increase
Solution Approach 1:
Multiple high-resolution texture images are combined into a single video sequence that is efficiently compressed. The video encoder exploits temporal similarities between frames to reduce the actual data transmitted, thereby maintaining 3D content quality while reducing network bandwidth requirements compared to delivering each texture image separately.
Solution Approach 2:
The delivery parameter changes from individual high-resolution images to a compressed video sequence. This parameter change enables the system to transmit the same amount of visual information with significantly reduced bandwidth consumption, as the video compression algorithm removes redundant information across frames while preserving quality.
4Loss of energy
If texture images are delivered as a video sequence, then compression efficiency is improved, but the complexity of processing and delivery increases
Solution Approach 1:
The video encoder and decoder are universal tools that can handle the texture sequence delivery. By using existing video encoding infrastructure, the system avoids the need for custom processing algorithms. The video encoder naturally handles the compression of multiple textures, and the decoder can render them individually, providing a simple and flexible solution.
Solution Approach 2:
The video sequence acts as an intermediary format between the source texture images and the final rendering. This intermediary approach simplifies the overall system by using well-established video encoding technology as a mediator, which handles the complexity of compression and delivery while maintaining the ability to process and render individual texture images when needed.
Data Source
AI summary
Techniques and systems are provided for generating a video from texture images, and for reconstructing the texture images from the video. For example, a texture image can be divided into a number of tiles, and the number of tiles can be sorted into a sequence of ordered tiles. The sequence of ordered tiles can be provided to a video coder for generating a coded video. The number of tiles can be encoded based on the sequence of ordered tiles. The encoded video including the encoded sequence of ordered tiles can be decoded. At least a portion of the decoded video can include the number of tiles sorted into a sequence of ordered tiles. A data file associated with at least the portion of the decoded video can be used to reconstruct the texture image using the tiles.