curioustorvald/tsvm
1
0
Fork 0
mirror of https://github.com/curioustorvald/tsvm.git synced 2026-06-11 07:14:04 +09:00
Code Issues Packages Projects Releases Wiki Activity

doc update

Browse Source
This commit is contained in:
minjaesong
2025-11-04 00:43:14 +09:00
parent 9d98cc1a21
commit 61b0bdaed7
1 changed files with 7 additions and 8 deletions
Download Patch File Download Diff File Expand all files Collapse all files

15
terranmon.txt
View File

@@ -677,7 +677,7 @@ TSVM Enhanced Video (TEV) Format
Created by CuriousTorvald and Claude on 2025-08-17 Created by CuriousTorvald and Claude on 2025-08-17
TEV is a modern video codec optimized for TSVM's 4096-color hardware, featuring TEV is a modern video codec optimized for TSVM's 4096-color hardware, featuring
DCT-based compression, motion compensation, and efficient temporal coding. DCT-based compression, optional motion compensation, and efficient temporal coding.
## Version History ## Version History
- Version 2.0: YCoCg-R 4:2:0 with 16x16/8x8 DCT blocks - Version 2.0: YCoCg-R 4:2:0 with 16x16/8x8 DCT blocks
@@ -1091,7 +1091,7 @@ This packet contains multiple frames encoded as a single spacetime block for opt
temporal compression. temporal compression.
uint8 Packet Type (0x12/0x13) uint8 Packet Type (0x12/0x13)
uint8 GOP Size (number of frames in this GOP, typically 16) uint8 GOP Size (number of frames in this GOP)
<if packet type is 0x13> <if packet type is 0x13>
uint32 Compressed Size uint32 Compressed Size
* Zstd-compressed Motion Data * Zstd-compressed Motion Data
@@ -1122,16 +1122,15 @@ This layout enables Zstd to find patterns across both spatial and temporal dimen
resulting in superior compression compared to per-frame encoding. resulting in superior compression compared to per-frame encoding.
### Motion Vectors ### Motion Vectors
- Stored in 1/16-pixel units (divide by 16.0 for pixel displacement) - Stored in 1/4-pixel units (divide by 4.0 for pixel displacement)
- Used for global motion compensation (camera movement, scene translation) - Computed using dense optical flow
- Computed using FFT-based phase correlation for accurate frame alignment
- Cumulative relative to frame 0 (not frame-to-frame deltas) - Cumulative relative to frame 0 (not frame-to-frame deltas)
- First frame (frame 0) always has motion vector (0, 0) - First frame (frame 0) always has motion vector (0, 0)
### Temporal 3D DWT Process ### Temporal 3D DWT Process
1. Detect inter-frame motion using phase correlation 1. Detect where the scene change is happening on the first pass
2. Align frames and expand canvas to preserve all original pixels 2. Determine GOP slicing from the scene detection
3. Apply 1D DWT across temporal axis (GOP frames) on expanded canvas 3. Apply 1D DWT across temporal axis (GOP frames)
4. Apply 2D DWT on each spatial slice of temporal subbands 4. Apply 2D DWT on each spatial slice of temporal subbands
5. Perceptual quantization with temporal-spatial awareness 5. Perceptual quantization with temporal-spatial awareness
6. Unified significance map preprocessing across all frames/channels 6. Unified significance map preprocessing across all frames/channels