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435 lines
22 KiB
Markdown
435 lines
22 KiB
Markdown
# CLAUDE.md
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This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
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## Project Overview
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**tsvm** is a virtual machine that mimics 8-bit era computer architecture and runs programs written in JavaScript. The project includes:
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- The virtual machine core
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- Reference BIOS implementation
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- TVDOS (operating system)
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- Videotron2K video display controller emulator
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- TerranBASIC integration
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- Multiple platform build system
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## Architecture
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### Core Components
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- **tsvm_core/**: Core virtual machine implementation in Kotlin
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- `VM.kt`: Main virtual machine class with memory management and peripheral slots
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- `peripheral/`: Hardware peripherals (graphics adapters, disk drives, TTY, audio, etc.)
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- `vdc/`: Videotron2K video display controller
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- Various delegates for JavaScript integration via GraalVM
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- **tsvm_executable/**: Main emulator application
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- `VMGUI.kt`: LibGDX-based GUI implementation
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- `TsvmEmulator.java`: Main application entry point
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- Menu systems for configuration, audio, memory management
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- **TerranBASICexecutable/**: TerranBASIC interpreter application
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- `TerranBASIC.java`: Entry point for BASIC interpreter
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- `VMGUI.kt`: GUI for BASIC environment
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### Key Technologies
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- **Kotlin/Java**: Primary implementation language
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- **LibGDX**: Graphics and windowing framework
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- **GraalVM**: JavaScript execution engine for running programs in the VM
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- **LWJGL**: Native library bindings
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- **IntelliJ IDEA**: Development environment (*.iml module files)
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### Virtual Hardware
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The VM emulates various peripherals through the `peripheral/` package:
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- Graphics adapters with different capabilities
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- Disk drives (including TevdDiskDrive for custom disk format)
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- TTY terminals and character LCD displays
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- Audio devices and MP2 audio environment
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- Network modems and serial interfaces
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- Memory management units
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## Build and Development
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### Building Applications
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Use the build scripts in `buildapp/`:
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- `build_app_linux_x86.sh` - Linux x86_64 AppImage
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- `build_app_linux_arm.sh` - Linux ARM64 AppImage
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- `build_app_mac_x86.sh` - macOS Intel
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- `build_app_mac_arm.sh` - macOS Apple Silicon
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- `build_app_windows_x86.sh` - Windows x86
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### Prerequisites
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1. Download JDK 17 runtimes to `~/Documents/openjdk/*` with specific naming:
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- `jdk-17.0.1-x86` (Linux AMD64)
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- `jdk-17.0.1-arm` (Linux Aarch64)
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- `jdk-17.0.1-windows` (Windows AMD64)
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- `jdk-17.0.1.jdk-arm` (macOS Apple Silicon)
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- `jdk-17.0.1.jdk-x86` (macOS Intel)
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2. Run `jlink` commands to create custom Java runtimes in `out/runtime-*` directories
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### Development Commands
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- **Build JAR**: Use IntelliJ IDEA build system to compile modules
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- **Run Emulator**: Execute `TsvmEmulator.java` main method or use built JAR
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- **Run TerranBASIC**: Execute `TerranBASIC.java` main method
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- **Package Apps**: Run appropriate build script from `buildapp/` directory
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### Assets and File System
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- `assets/disk0/`: Virtual disk content including TVDOS system files
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- `assets/bios/`: BIOS ROM files and implementations
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- `My_BASIC_Programs/`: Example BASIC programs for testing
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- TVDOS filesystem uses custom format with specialised drivers
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## Videotron2K
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The Videotron2K is a specialised video display controller with:
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- Assembly-like programming language
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- 6 general registers (r1-r6) and special registers (tmr, frm, px, py, c1-c6)
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- Scene-based programming model
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- Drawing commands (plot, fillin, goto, fillscr)
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- Conditional execution with postfixes (zr, nz, gt, ls, ge, le)
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Programs are structured with SCENE blocks and executed with perform commands.
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## Memory Management
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- VM supports up to USER_SPACE_SIZE memory
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- 64-byte malloc units with reserved blocks
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- Peripheral slots (1-8 configurable)
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- Memory-mapped I/O for peripheral access
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- JavaScript programs run in sandboxed GraalVM context
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### Peripheral Memory Addressing
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Peripheral memories can be accessed using `vm.peek()` and `vm.poke()` functions, which takes absolute address.
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- Peripherals take up negative number of the memory space, and their addressing is in backwards (e.g. Slot 1 starts at -1048577 and ends at -2097152)
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- Peripherals take up two memory regions: MMIO area and Memory Space area; MMIO is accessed by PeriBase (and its children) using `mmio_read()` and `mmio_write()`, and the Memory Space is accessed using `peek()` and `poke()`.
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- Peripheral at slot *n* takes following addresses
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1. MMIO area (-131072×n)-1 to -131072×(n+1)
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2. Memory Space area -(1048576×n)-1 to (-1048576×(n+1))
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## Testing
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- Use example programs in `My_BASIC_Programs/` for BASIC testing
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- JavaScript test programs available in `assets/disk0/`
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- Videotron2K assembly examples in documentation
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## Notes
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- The 'gzip' namespace in TSVM's JS programs is a misnomer: the actual 'gzip' functions (defined in CompressorDelegate.kt) call Zstd functions.
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## TVDOS
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### TVDOS Movie Formats
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#### Legacy iPF Format
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- Format documentation on `terranmon.txt` (search for "TSVM MOV file format" and "TSVM Interchangeable Picture Format (aka iPF Type 1/2)")
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- Video Encoder implementation on `assets/disk0/tvdos/bin/encodemov.js` (iPF Format 1 and 2) and `assets/disk0/tvdos/bin/encodemov2.js` (iPF Format 1-delta)
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- Actual encoding/decoding code is in `GraphicsJSR223Delegate.kt`
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- Audio uses standard MP2
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#### TEV Format (TSVM Enhanced Video)
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- **Modern video codec** optimized for TSVM hardware with 60-80% better compression than iPF
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- **C Encoder**: `video_encoder/encoder_tev.c` - Hardware-accelerated encoder with motion compensation and DCT
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- How to build: `make clean && make`
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- **Rate Control**: Supports both quality mode (`-q 0-4`) and bitrate mode (`-b N` kbps)
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- **JS Decoder**: `assets/disk0/tvdos/bin/playtev.js` - Native decoder for TEV format playback
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- How to build: `must be done manually by the user; the TSVM is not machine-interactable`
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- **Hardware accelerated decoding**: Extended GraphicsJSR223Delegate.kt with TEV functions:
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- `tevDecode()` - The main decoding function (now accepts rate control factor)
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- `tevIdct8x8()` - Fast 8×8 DCT transforms
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- `tevMotionCopy8x8()` - Sub-pixel motion compensation
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- **Features**:
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- 16×16 DCT blocks (vs 4×4 in iPF) for better compression
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- Motion compensation with ±8 pixel search range
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- YCoCg-R 4:2:0 Chroma subsampling (more aggressive quantisation on Cg channel)
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- Full 8-Bit RGB colour for increased visual fidelity, rendered down to TSVM-compliant 4-Bit RGB with dithering upon playback
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- **Usage Examples**:
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```bash
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# Quality mode
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./encoder_tev -i input.mp4 -q 2 -o output.tev
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# Playback
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playtev output.tev
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```
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- **Format documentation**: `terranmon.txt` (search for "TSVM Enhanced Video (TEV) Format")
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- **Version**: 2.1 (includes rate control factor in all video packets)
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#### TAV Format (TSVM Advanced Video)
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- **Successor to TEV**: DWT-based video codec using wavelet transforms instead of DCT
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- **C Encoder**: `video_encoder/encoder_tav.c` - Multi-wavelet encoder with perceptual quantisation
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- How to build: `make tav`
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- **Wavelet Support**: Multiple wavelet types for different compression characteristics
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- **JS Decoder**: `assets/disk0/tvdos/bin/playtav.js` - Native decoder for TAV format playback
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- **Hardware accelerated decoding**: Extended GraphicsJSR223Delegate.kt with TAV functions
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- **Packet analyser**: `video_encoder/tav_inspector.c` - Debugging tool that parses TAV packets into human-readable form
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- **Features**:
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- **Multiple Wavelet Types**: 5/3 reversible, 9/7 irreversible, CDF 13/7, DD-4, Haar
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- **Single-tile encoding**: One large DWT tile for optimal quality (no blocking artifacts)
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- **Perceptual quantisation**: HVS-optimized coefficient scaling
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- **YCoCg-R color space**: Efficient chroma representation with "simulated" subsampling using anisotropic quantisation (search for "ANISOTROPY_MULT_CHROMA" on the encoder)
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- **6-level DWT decomposition**: Deep frequency analysis for better compression (deeper levels possible but 6 is the maximum for the default TSVM size)
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- **Significance Map Compression**: Improved coefficient storage format exploiting sparsity for 16-18% additional compression (2025-09-29 update)
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- **Concatenated Maps Layout**: Cross-channel compression optimization for additional 1.6% improvement (2025-09-29 enhanced)
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- **Usage Examples**:
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```bash
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# Different wavelets
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./encoder_tav -i input.mp4 -w 0 -q 2 -o output.tav # 5/3 reversible (lossless capable)
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./encoder_tav -i input.mp4 -w 1 -q 2 -o output.tav # 9/7 irreversible (default, best compression)
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./encoder_tav -i input.mp4 -w 2 -q 2 -o output.tav # CDF 13/7 (experimental)
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./encoder_tav -i input.mp4 -w 16 -q 2 -o output.tav # DD-4 (four-point interpolating)
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./encoder_tav -i input.mp4 -w 255 -q 2 -o output.tav # Haar (demonstration)
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# Quality levels (0-5)
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./encoder_tav -i input.mp4 -q 0 -o output.tav # Lowest quality, smallest file
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./encoder_tav -i input.mp4 -q 5 -o output.tav # Highest quality, largest file
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# Temporal 3D DWT (GOP-based encoding)
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./encoder_tav -i input.mp4 --temporal-dwt -q 2 -o output.tav
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# Playback
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playtav output.tav
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```
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**CRITICAL IMPLEMENTATION NOTES**:
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**Wavelet Coefficient Layout**:
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- TAV uses **2D Spatial Layout** in memory: `[LL, LH, HL, HH, LH, HL, HH, ...]` for each decomposition level
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- **Forward transform must output**: `temp[0...half-1] = low-pass`, `temp[half...length-1] = high-pass`
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- **Inverse transform must expect**: Same 2D spatial layout and exactly reverse forward operations
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- **Common mistake**: Assuming linear layout leads to grid/checkerboard artifacts
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**Wavelet Implementation Pattern**:
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- All wavelets must follow the **exact same structure** as the working 5/3 implementation:
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```c
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// Forward: 1. Predict step, 2. Update step
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temp[half + i] = data[odd_index] - prediction; // High-pass
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temp[i] = data[even_index] + update; // Low-pass
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// Inverse: Reverse order - 1. Undo update, 2. Undo predict
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temp[i] -= update; // Undo low-pass update
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temp[half + i] += prediction; // Undo high-pass predict
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```
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- **Boundary handling**: Use symmetric extension for filter taps beyond array bounds
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- **Reconstruction**: Interleave even/odd samples: `data[2*i] = low[i], data[2*i+1] = high[i]`
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**Debugging Grid Artifacts**:
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- **Symptom**: Checkerboard or grid patterns in decoded video
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- **Cause**: Mismatch between encoder/decoder coefficient layout or lifting step operations
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- **Solution**: Ensure forward and inverse transforms use identical coefficient indexing and reverse operations exactly
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**Supported Wavelets**:
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- **0**: 5/3 reversible (lossless when unquantised, JPEG 2000 standard)
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- **1**: 9/7 irreversible (best compression, CDF 9/7 variant, default choice)
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- **2**: CDF 13/7 (experimental, simplified implementation)
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- **16**: DD-4 (four-point interpolating Deslauriers-Dubuc, for still images)
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- **255**: Haar (demonstration only, simplest possible wavelet)
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- **Format documentation**: `terranmon.txt` (search for "TSVM Advanced Video (TAV) Format")
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- **Version**: Current (perceptual quantisation, multi-wavelet support, significance map compression)
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#### TAV Significance Map Compression (Technical Details)
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The significance map compression technique implemented on 2025-09-29 provides substantial compression improvements by exploiting the sparsity of quantised DWT coefficients:
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**Implementation Files**:
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- **C Encoder**: `video_encoder/encoder_tav.c` - `preprocess_coefficients()` function (lines 960-991)
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- **C Decoder**: `video_encoder/decoder_tav.c` - `postprocess_coefficients()` function (lines 29-48)
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- **Kotlin Decoder**: `GraphicsJSR223Delegate.kt` - `postprocessCoefficients()` function for TSVM runtime
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**Technical Approach**:
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```
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Original: [coeff_array] → [concatenated_significance_maps + nonzero_values]
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Concatenated Maps Layout:
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[Y_map][Co_map][Cg_map][Y_vals][Co_vals][Cg_vals] (channel layout 0)
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[Y_map][Co_map][Cg_map][A_map][Y_vals][Co_vals][Cg_vals][A_vals] (channel layout 1)
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[Y_map][Y_vals] (channel layout 2)
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[Y_map][A_map][Y_vals][A_vals] (channel layout 3)
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[Co_map][Cg_map][Co_vals][Cg_vals] (channel layout 4)
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[Co_map][Cg_map][A_map][Co_vals][Cg_vals][A_vals] (channel layout 5)
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(replace Y->I, Co->Ct, Cg->Cp for ICtCp colour space)
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- Significance map: 1 bit per coefficient (0=zero, 1=non-zero)
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- Value arrays: Only non-zero coefficients in sequence per channel
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- Cross-channel optimization: Zstd finds patterns across similar significance maps
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- Result: 16-18% compression improvement + 1.6% additional from concatenation
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```
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**Performance**:
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- **Sparsity exploitation**: Tested on quantised DWT coefficients with 86.9% sparsity (Y), 97.8% (Co), 99.5% (Cg)
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- **Compression improvement**: 16.4% from significance maps + 1.6% from concatenated layout
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- **Real-world impact**: 559 bytes saved per frame (5.59 MB per 10k frames)
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- **Cross-channel benefit**: Concatenated maps allow Zstd to exploit similarity between significance patterns
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#### TAV Temporal 3D DWT (GOP Unified Encoding)
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Implemented on 2025-10-15 for improved temporal compression through group-of-pictures (GOP) encoding:
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**Key Features**:
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- **3D DWT**: Applies DWT in both spatial (2D) and temporal (1D) dimensions for optimal spacetime compression
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- **Unified GOP Preprocessing**: Single significance map for all frames and channels in a GOP (width×height×N_frames×3_channels)
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- **FFT-based Phase Correlation**: Uses FFTW3 library for accurate global motion estimation with quarter-pixel precision
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- **GOP Size**: Typically 8 frames (configurable), with scene change detection for adaptive GOPs
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- **Single-frame Fallback**: GOP size of 1 automatically uses traditional I-frame encoding
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**Packet Format**:
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- **0x12 (GOP_UNIFIED)**: `[gop_size][motion_vectors...][compressed_size][compressed_data]`
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- Motion vectors stored as int16_t in quarter-pixel units for all frames in GOP
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- Unified significance map for entire GOP block enables cross-frame compression
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- **0xFC (GOP_SYNC)**: `[frame_count]` - Indicates N frames were decoded from GOP block
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- **Timecode Emission**: One timecode packet per GOP (not per frame)
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**Technical Implementation**:
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```c
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// Unified preprocessing structure (encoder_tav.c:2371-2509)
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[All_Y_maps][All_Co_maps][All_Cg_maps][All_Y_values][All_Co_values][All_Cg_values]
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// Where maps are grouped by channel across all GOP frames for optimal Zstd compression
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// Phase correlation using FFT (encoder_tav.c:1246-1383)
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// - FFTW3 forward FFT on grayscale frames
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// - Cross-power spectrum computation
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// - Inverse FFT gives correlation peak at (dx, dy)
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// - Parabolic interpolation for quarter-pixel refinement
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```
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**Usage**:
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```bash
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# Enable temporal 3D DWT
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./encoder_tav -i input.mp4 --temporal-dwt -q 2 -o output.tav
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# Inspect GOP structure
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./tav_inspector output.tav -v
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```
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**Compression Benefits**:
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- **Temporal Coherence**: Exploits similarity across consecutive frames
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- **Unified Compression**: Zstd compresses entire GOP as single block, finding patterns across time
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- **Motion Compensation**: FFT-based phase correlation provides accurate global motion estimation
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- **Adaptive GOPs**: Scene change detection ensures optimal GOP boundaries
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#### TAD Format (TSVM Advanced Audio)
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- **Perceptual audio codec** for TSVM using CDF 9/7 biorthogonal wavelets
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- **C Encoder**: `video_encoder/encoder_tad.c` - Core Encoder library; `video_encoder/encoder_tad_standalone.c` - Standalone encoder with FFmpeg integration
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- How to build: `make tad`
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- **Quality Levels**: 0-5 (0=lowest quality/smallest, 5=highest quality/largest; designed to be in sync with TAV encoder)
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- **C Decoders**:
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- `video_encoder/decoder_tad.c` - Shared decoder library with `tad32_decode_chunk()` function
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- `video_encoder/decoder_tad.h` - Exports shared decoder API
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- `video_encoder/decoder_tav.c` - TAV decoder that uses shared TAD decoder for audio packets
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- **Shared Architecture** (Fixed 2025-11-10): Both standalone TAD and TAV decoders now use the same `tad32_decode_chunk()` implementation, eliminating code duplication and ensuring identical output
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- **Kotlin Decoder**: `AudioAdapter.kt` - Hardware-accelerated TAD decoder for TSVM runtime
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- **Quantisation Fix** (2025-11-10): Fixed BASE_QUANTISER_WEIGHTS to use channel-specific 2D array (Mid/Side) instead of single 1D array, resolving severe audio distortion
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- **Features**:
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- **32 KHz stereo**: TSVM audio hardware native format
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- **Variable chunk sizes**: Any size ≥1024 samples, including non-power-of-2 (e.g., 32016 for TAV 1-second GOPs)
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- **Pre-emphasis filter**: First-order IIR filter (α=0.5) shifts quantisation noise to lower frequencies
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- **Gamma compression**: Dynamic range compression (γ=0.5) before quantisation
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- **M/S stereo decorrelation**: Exploits stereo correlation for better compression
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- **9-level CDF 9/7 DWT**: Fixed 9 decomposition levels for all chunk sizes
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- **Perceptual quantisation**: Channel-specific (Mid/Side) frequency-dependent weights with lambda companding (λ=6.0)
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- **EZBC encoding**: Binary tree embedded zero block coding exploits coefficient sparsity (86.9% Mid, 97.8% Side)
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- **Zstd compression**: Level 7 on concatenated EZBC bitstreams for additional compression
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- **Non-power-of-2 support**: Fixed 2025-10-30 to handle arbitrary chunk sizes correctly
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- **Usage Examples**:
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```bash
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# Encode with default quality (Q3)
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encoder_tad -i input.mp4 -o output.tad
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# Encode with highest quality
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encoder_tad -i input.mp4 -o output.tad -q 5
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# Encode without Zstd compression
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encoder_tad -i input.mp4 -o output.tad --no-zstd
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# Verbose output with statistics
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encoder_tad -i input.mp4 -o output.tad -v
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# Decode back to PCM16
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decoder_tad -i input.tad -o output.pcm
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```
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- **Format documentation**: `terranmon.txt` (search for "TSVM Advanced Audio (TAD) Format")
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- **Version**: 1.1 (EZBC encoding with non-power-of-2 support, updated 2025-10-30; decoder architecture and Kotlin quantisation weights fixed 2025-11-10; documentation updated 2025-11-10 to reflect pre-emphasis and EZBC)
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**TAD Encoding Pipeline**:
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1. **Pre-emphasis filter** (α=0.5) - Shifts quantisation noise toward lower frequencies
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2. **Gamma compression** (γ=0.5) - Dynamic range compression
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3. **M/S decorrelation** - Transforms L/R to Mid/Side
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4. **9-level CDF 9/7 DWT** - Wavelet decomposition (fixed 9 levels)
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5. **Perceptual quantisation** - Lambda companding (λ=6.0) with channel-specific weights
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6. **EZBC encoding** - Binary tree embedded zero block coding per channel
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7. **Zstd compression** (level 7) - Additional compression on concatenated EZBC bitstreams
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**TAD Compression Performance**:
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- **Target Compression**: 2:1 against PCMu8 baseline (4:1 against PCM16LE input)
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- **Achieved Compression**: 2.51:1 against PCMu8 at quality level 3
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- **Audio Quality**: Preserves full 0-16 KHz bandwidth
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- **Coefficient Sparsity**: 86.9% zeros in Mid channel, 97.8% in Side channel (typical)
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- **EZBC Benefits**: Exploits sparsity, progressive refinement, spatial clustering
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**TAD Integration with TAV**:
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TAD is designed as an includable API for TAV video encoder integration. The variable chunk size
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support enables synchronized audio/video encoding where audio chunks can match video GOP boundaries.
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TAV embeds TAD-compressed audio using packet type 0x24 with Zstd compression.
|
||
|
||
**TAD Hardware Acceleration**:
|
||
TSVM accelerates TAD decoding with AudioAdapter.kt (backend) and AudioJSR223Delegate.kt (API):
|
||
- Backend decoder in AudioAdapter.kt with non-power-of-2 chunk size support (fixed 2025-10-30)
|
||
- API functions in AudioJSR223Delegate.kt for JavaScript access
|
||
- Supports chunk sizes from 1024 to 32768+ samples (any size ≥1024)
|
||
- Fixed 9-level CDF 9/7 inverse DWT with correct length tracking for non-power-of-2 sizes
|
||
|
||
**Critical Implementation Note (Fixed 2025-10-30)**:
|
||
Multi-level inverse DWT must pre-calculate the exact sequence of lengths from forward transform:
|
||
```kotlin
|
||
val lengths = IntArray(levels + 1)
|
||
lengths[0] = chunk_size
|
||
for (i in 1..levels) {
|
||
lengths[i] = (lengths[i - 1] + 1) / 2
|
||
}
|
||
// Apply inverse DWT using lengths[level] for each level
|
||
```
|
||
Using simple doubling (`length *= 2`) is incorrect for non-power-of-2 sizes and causes
|
||
mirrored subband artifacts.
|
||
|
||
**TAD Decoding Pipeline**:
|
||
1. **Zstd decompression** - Decompress concatenated EZBC bitstreams
|
||
2. **EZBC decoding** - Binary tree decoder reconstructs quantised int8 coefficients per channel
|
||
3. **Lambda decompanding** - Inverse Laplacian CDF mapping with channel-specific weights
|
||
4. **9-level inverse CDF 9/7 DWT** - Wavelet reconstruction with proper non-power-of-2 length tracking
|
||
5. **M/S to L/R conversion** - Transform Mid/Side back to Left/Right
|
||
6. **Gamma expansion** (γ⁻¹=2.0) - Restore dynamic range
|
||
7. **De-emphasis filter** (α=0.5) - Reverse pre-emphasis, remove frequency shaping
|
||
8. **PCM32f to PCM8** - Noise-shaped dithering for final 8-bit output
|
||
|
||
**Critical Quantisation Weights Note (Fixed 2025-11-10)**:
|
||
The TAD decoder MUST use channel-specific quantisation weights for Mid (channel 0) and Side (channel 1) channels. The Kotlin decoder (AudioAdapter.kt) originally used a single 1D weight array, which caused severe audio distortion. The correct implementation uses a 2D array:
|
||
|
||
```kotlin
|
||
// CORRECT (Fixed 2025-11-10)
|
||
private val BASE_QUANTISER_WEIGHTS = arrayOf(
|
||
floatArrayOf( // Mid channel (0)
|
||
4.0f, 2.0f, 1.8f, 1.6f, 1.4f, 1.2f, 1.0f, 1.0f, 1.3f, 2.0f
|
||
),
|
||
floatArrayOf( // Side channel (1)
|
||
6.0f, 5.0f, 2.6f, 2.4f, 1.8f, 1.3f, 1.0f, 1.0f, 1.6f, 3.2f
|
||
)
|
||
)
|
||
|
||
// During dequantisation:
|
||
val weight = BASE_QUANTISER_WEIGHTS[channel][sideband] * quantiserScale
|
||
coeffs[i] = normalisedVal * TAD32_COEFF_SCALARS[sideband] * weight
|
||
```
|
||
|
||
The different weights for Mid and Side channels reflect the perceptual importance of different frequency bands in each channel. Using incorrect weights causes:
|
||
- DC frequency underamplification (using 1.0 instead of 4.0/6.0)
|
||
- Incorrect stereo imaging and extreme side channel distortion
|
||
- Severe frequency response errors that manifest as "clipping-like" distortion
|