AI is hungry for images. Feeding it uncompressed data is the expensive way.

Every new vision model is trained on more frames, at higher resolution, from more cameras and sensors. Archives grow just as fast, and both have to be kept for years.

Most of that data is still stored uncompressed, because nobody wants to risk losing the detail a model or a future restoration will need. The cost shows up everywhere: storage that keeps expanding, uploads to the cloud that take days, and expensive GPUs waiting on disks and networks instead of computing.

intoPIX compression removes that trade-off. Bit-exact or near-lossless coding shrinks the data where it is created and keeps it small all the way to the model, with codecs light enough to decode at full speed on standard CPUs and GPUs.

Every terabyte now costs more

AI data centers are absorbing a large share of the world's flash memory and DRAM. SSD and memory prices have climbed sharply since late 2025, and manufacturers expect supply to stay tight for years.

The drive is only the start

Each terabyte is mirrored, backed up, powered, cooled and kept for years. In the cloud it is billed every month, and again every time it moves.

Data grows faster than budgets

Higher resolutions, more cameras and sensors, longer retention. Without compression, storage spending grows with every new project.

Compression is capacity you don't buy

Store the same content in 2 to 10 times less space, on the drives, loggers, and memory you already have.

What 1 TB of storage holds ...

Uncompressed	1 TB
Mathematically lossless	2-3 TB
Near-lossless	4-10 TB

Equivalent uncompressed content. Actual ratios depend on content, bit depth and format.

Lighter data, more efficient AI

Compress once at the source.

Everything that follows (storing, transferring, loading, training) handles a fraction of the data.

Bigger datasets, same storage

Keep 2 to 10 times more training data or archive footage on the drives, servers and cloud buckets you already pay for.

GPUs that stay busy

Less data to read per frame means less waiting on disks and networks. JPEG XS and TicoRAW decode fast on standard CPUs and GPUs, so decoding keeps pace with training.

Quality tuned for machines

Near-lossless modes preserve the detail that detection, segmentation and measurement depend on. intoPIX research shows JPEG XS coding can be tuned to the vision task itself.

Compressed at the source

FPGA and ASIC IP cores compress in real time inside cameras and data loggers, with sub-millisecond latency, before the data ever reaches storage.

Keep the RAW, re-train later

TicoRAW stores Bayer sensor data, not a processed picture. Datasets can be re-processed for new ISPs and new models without a new capture campaign.

Less energy, smaller footprint

Fewer drives to power and cool, less data crossing networks, and codecs designed for low power. Efficiency gained at every stage of the pipeline.

Choose how much you keep

One family of standard codecs, two ways to compress, depending on whether every bit must survive or every useful detail.

2–3× less data
Mathematically lossless

Bit-to-bit reversible.

The decoded file is identical to the original, every time.

Keeps	Every bit of the original
For	Master archives, medical and scientific records, reference datasets, regulatory evidence
Codecs	JPEG XS, TicoRAW (incl. JPEG XS RAW)

4–10× less data
Near-lossless

Lossless to the eye, or to the machine.

No visible artifacts and no measurable effect on the task.

Keeps	All detail that people or AI models use
For	AI training and validation datasets, ADAS logs, inspection imagery, production archives
Codecs	JPEG XS, TicoRAW (incl. JPEG XS RAW)

Both modes are wavelet-based and support resolution scalability: extract a small proxy from the full-quality file for browsing, labeling and dataset curation, without decoding the whole frame.