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Novus Examples
tga705.1 KB

Single running shoe on a plain white (tga rle)

The same 512x512 photograph written as tga rle. Run-length encoded Targa. Every member of this group is identical pixels, so a decoder returning different dimensions or a different pixel count has lost something rather than simply produced a different file; the 24-bit PNG in this group is lossless and is the reference the lossy members are measured against Source plate pr-shoe.

Rendered preview of Single running shoe on a plain white (tga rle)

Rendered preview of the tga file (705.1 KB). Download above for the original.

Specifications

Compression
rle
Lossless
true
Width
512
Height
512
Variant
tga-rle
Source Plate
pr-shoe.png
Synthetic
true
Disclosure
AI-generated / synthetic. Not a photograph of a real person, place or event.
Schema Version
1
Alt Text
Single running shoe on a plain white studio background, soft light
Alt Text Source
prompt

Testing contract

Expected to pass
Scenario
Decode this file and the 24-bit PNG in the same group, compare dimensions and pixel count, then check the decoded properties against the ones declared in its specs.
Expected result
It decodes to 512x512 with rle compression. The encode is lossless, so it must decode to the reference pixel for pixel. Every member of this group is the same source image, so a reader that returns different dimensions has demonstrably lost something rather than read a different file.

What is a .tga file?

TGA (Truevision Targa) is a raster format supporting 8- to 32-bit pixels with optional run-length encoding and an alpha channel. Long used in video, gaming, and 3D texturing, it stores simple uncompressed or RLE-compressed image data. It remains common as a texture and intermediate format.

How to use this file

Use an example TGA to test texture loaders in game and 3D pipelines, RLE decompression, and converters that read Targa alpha and orientation flags.

How to use this file for testing

“Single running shoe on a plain white (tga rle)” is a deterministic Novus Examples fixture for Media handling. Generated stills, clips and audio published in several encodings of identical content, so a media pipeline can be tested on format handling, quality loss and frame or sample extraction against a reference that is known rather than assumed.

Documented properties for this file: 512×512. Compare results against paired or grouped companions on this page when present (clean↔damaged, searchable↔scanned, or format twins) so scores stay reproducible across runs.

Download the file once, keep the path stable in CI or local scripts, and treat the spec table as the contract: dimensions, seeds, field lists, and roles are intentional. Corrupt or invalid samples are labelled as such, expect parsers to fail loudly rather than silently accept them.

For image AI or enhancement tools, run the model or filter on this file and diff against the clean or ground-truth companion in the same group when available. Keep seeds and documented damage parameters in your evaluation notes so regressions are attributable.

Code examples

<img src="pr-shoe-tga-rle.tga" alt="Example image" width="640" loading="lazy">

Generated by generation/image_format_wave.py. Free for any use, no attribution required, license.