Hardware Efficient Lossless Realtime Compression of Raw Image Data

Sammanfattning: When transmitting data it is often desired to lower the bitrate in the transmission. In video capture devices, such as video cameras, with a high resolution and high capturing rate the bitrate in the transmission is high and it is often desired to lower this bitrate. Some of the constraints that were put on the compression techniques was that no information could be lost in the compression, no significant amount of delay may be introduced and that the compression techniques should have as small hardware requirements as possible. Another limitation was that the compression techniques were limited to store less than one frame of memory. One exception of this was tested to see how the compression ratio would be improved when compressing using more than a frame of memory. How transmission in a single directional transmission link with compressed data could be done has also been investigated during this thesis. The compression techniques in this paper are two-step based compression techniques called linear prediction coding. The first step is to predict the pixel values in the image and the second step is to encode the prediction error. In order to recover the image, these codes need to be decoded to recover the prediction error which can then be used to reconstruct the image. This is an efficient compression technique due to that there is much redundant information in an image and instead of encoding each pixel value it is more efficient to encode the differences between pixels. The differences contains the same information but may be encoded using shorter codewords. The average compression ratios for the techniques presented in this paper achieves a compression ratio close to 40% when compressing raw image data. If noise reduction techniques are applied to the image to compress, the compression techniques may improve their compression ratio with over 10%. Another way to improve the compression, without losing any significant amount of information, is presented in this paper and it is done by measuring the noise in the image and then allowing some quantization based on this measurement. A solution to the problem on how to transmit data in a safe way from the encoder to the decoder have been suggested. The data is sent in packets where each packet contains a header field and a data field. The prediction errors are encoded in the data field and the number of symbols encoded in the data field are represented in the header field. These two got a fixed number of bytes and by using error correcting code, the data can be safely be transmitted