It really depends when and how you compress in the pipeline, but ultimately it makes sense to archive the original quality, as storage is usually far less expensive than reshooting. Even if that is the case, is there a way to extract individual frames from the yuv4mpeg uncompressed stream so that I can compare them with the frames in the x264 stream, or at least to decode the yuv4mpeg back from the x264 stream. This is troubling, because all the information I have found on lossless predictive coding --qp 0 claims that the whole encoding should be lossless, but I am unable to verifiy this. The first selection value in the table, zero, is only used for differential coding in the hierarchical mode of operation. The three neighboring samples must be already encoded samples.
Part 2, released in 2003, introduced extensions such as. This spreads out the excess coding length over many symbols. The planes are a set of Y bytes, then the U and V bytes where U and V use 128 as the zero value. The standard cannot encode or decode it, but Ken Murchison of Oceana Matrix Ltd. Lossless data compression is not worth it in most cases because lossy formats can give you the exact quality at a lower cost file size. Once all the samples are predicted, the differences between the samples can be obtained and entropy-coded in a lossless fashion using or.
Here is a comparison of the same image but encoded differently with a lossy and lossless format. Flip back and forth, you can see a faint difference but barely. So far I haven't been able to get back to my exact original videos with x264 now it likes my input format. If h264 can be truly lossless they should be identical. In the process, the predictor combines up to three neighboring samples at A, B, and C shown in Fig. Total cannot be achieved by first order entropy of the prediction residuals employed by these inferior standards. And it is much simpler and safer than a whole video processing chain.
Though this file is not supported natively by Apple, it can be re-encoded as near-lossless for playback. The division-free bias computation procedure is demonstrated in. While x264 does accept raw 4:2:0 pixels in a file, it is really quite difficult getting 4:4:4 pixels passed in. I tried x264 but it crashes. The downside is the much slower speed, and that you have to extract before seeing it. .
If x264 does lossless encoding but doesn't like your input format, then your best bet is to use ffmpeg to deal with the input file. This is a model in which predictions of the sample values are estimated from the neighboring samples that are already coded in the image. There's a pretty comprehensive list out there. By lossless, I mean that if I feed it a series of frames and encode them, and then if I extract all the frames from the encoded video, I will get the exact same frames as in the input, pixel by pixel, frame by frame. The purpose of the quantization is to maximize the mutual information between the current sample value and its context such that the high-order dependencies can be captured. But for images, I think you should try it. There are always lingering very very subtle artifacts, so maybe ffmpeg can help me there.
It uses a predictive scheme based on the three nearest causal neighbors upper, left, and upper-left , and coding is used on the prediction error. The differences from one sample to the next are usually close to zero. To avoid having excess code length over the entropy, one can use alphabet extension which codes blocks of symbols instead of coding individual symbols. Any one of the predictors shown in the table below can be used to estimate the sample located at X. A bias estimation could be obtained by dividing cumulative prediction errors within each context by a count of context occurrences.
Each one of the differences found in the above equation is then quantized into roughly equiprobable and connected regions. Is it possible to do completely lossless encoding in h264? It uses x87 floating point. And yes, most lossless video codecs are designed for realtime capture and thus are fast, and usually compress natural fotage better than general purpose archivers, but not by much. They didn't match my backup. I extracted all the frames from that video and kept a copy. The block in the figure acts as a storage of the current sample which will later be a previous sample. Notice that these differences are closely related to the statistical behavior of prediction errors.
My point is that you would never tell the difference in quality without looking at both of them at the same time and examining them closely. Howerver, even with --qp 0 and a rgb or yv12 source I still get some differences, minimal but present. I have decided to make this patch available via my ftp site. I decoded the errors as well, but now I have an array of errors without knowing how to go back to the original image. Note that selections 1, 2, and 3 are one-dimensional predictors and selections 4, 5, 6, and 7 are two-dimensional predictors.
There is also lossless image formats, like. Any one of the eight predictors listed in the table can be used. Its special case with the optimal encoding value 2 k allows simpler encoding procedures. I tried doing the following to discard colorspace reduction: Encode video using lagarith lossless yv12 so that I would have my images in a colorspace suitable for h264. Prediction refinement can then be done by applying these estimates in a feedback mechanism which eliminates prediction biases in different contexts. Most predictors take the average of the samples immediately above and to the left of the target sample.