GoPro CineForm Studio provides users with professional tools for converting and editing their photos. Especially designed for GoPro camera outputs video and photos , the application allows you to trim video clips, create videos from timelapse photos, change slow motion settings and adjust the images exposure, contrast, saturation in order to enhance their quality.
GoPro CineForm Studio is simple and easy-to-use video editing for certain models of camera. The problem is there are no hardware based compression solutions that could be put inside a GoPro-sized camera. Popular standards like H. Today there are no multiple-lens cameras that can compress to a single image beyond 4K. Throughout the history of GoPro, our cameras have been on the cutting-edge of technology, producing content that was well beyond the capabilities of consumer hardware to support.
When these resolutions were first introduced, many consumer laptops weren't equipped with hardware to offer smooth playback, so the CineForm codec was used behind the scenes to make playback and editing smoother and faster. This has been the role of CineForm even before its acquisition by GoPro in The CineForm compression format was designed for speed on personal computers, so it could bridge the gap between a cutting-edge camera and the existing computer hardware that needed to present the images.
CineForm has stayed well ahead of the video standards which take a long time to arrive in hardware, from our laptops to our smartphones. For the upcoming Fusion, just like with Omni, the video is stitched after capture using CineForm to hold more than today's standards can deliver. If you store data with low frequencies low pass on the left and the high frequencies high pass on the right you get the image below.
A low pass image is basically the average, and high pass image is like an edge enhance. For a two level wavelet, you repeat the same horizontal and vertical wavelet operations of the top left quadrant to provide:. All that grey is easy to compress.
The reason there is very little information seen in these high frequency regions of the image generated from WaveletDemo is the high frequency data has been quantized.
The human eye is not very good at seeing subtle changes in high frequency regions, so this is exploited by scaling the high-frequency samples before they are stored:.
After the wavelet and quantization stages, you have the same number of samples as the original source. The compression is achieved as the samples are no longer evenly distributed after wavelet and quantization. There are many many zeros and ones, than higher values, so we can store all these values more efficiently, often up to 10 times more so. The output of the quantization stage has a lot of zeros, and many in a row.
After all previous steps the high frequency samples are stored with a variable length coding scheme. In CineForm classic Huffman coding is used. Again a lot of theory is turned into a table which maps sample values to codewords with differing bit lengths — not a lot of complexity.
While I showed that the steps involved are fairly simple, and much can be modeled in only lines of source code WavetletDemo , the CineForm SDK is currently over k lines of code.
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