VC-6

[2] Typically, these applications involve compressing finished compositions for editing, contribution, primary distribution, archiving and other applications where it is necessary to preserve image quality as close to the original as possible, whilst reducing bitrates, and optimizing processing, power and storage requirements.

The codec is based on hierarchical data structures called s-trees, and does not involve DCT or wavelet transform compression.

[8] In the VC-6 standard[2] an up-sampler developed with an in-loop Convolutional Neural Network is provided to optimize the detail in the reconstructed image, without requiring a large computational overhead.

The ability to navigate spatially within the VC-6 bitstream at multiple levels[2] also provides the ability for decoding devices to apply more resources to different regions of the image allowing for Region-of-Interest applications to operate on compressed bitstreams without requiring a decode of the full-resolution image.

[9] At the NAB Show in 2015, V-Nova claimed "2x–3x average compression gains, at all quality levels, under practical real-time operating scenarios versus H.264, HEVC and JPEG2000.".

[10] Making this announcement on 1 April before a major trade show attracted the attention of many compression experts.

[11] Since then, V-Nova have deployed and licensed the technology, known at the time as Perseus,[10] in both contribution and distribution applications around the world including Sky Italia,[12] Fast Filmz,[13][14] Harmonic Inc, and others.

To compress and decompress the data in each plane, VC-6 uses hierarchical representations of small tree-like structure that carry metadata used to predict other trees.

Compression is achieved in an s-tree by using metadata to signal whether levels can be predicted with selective carrying of enhancement data in the bitstream.

Next, desparsification and entropy decoding processes are performed to fill the grid with data values at each coordinate.

[2] The standard[2] defines a number of basic upsamplers[20] to create higher-resolution predictions from lower-resolution echelons.