compression

I am doing following to do compression of o/p files from reducer: OutputStream out = ipFs.create( new Path( opDir + "/" + fileName ) ); CompressionCodec codec = new GzipCodec(); OutputStream cs = codec.createOutputStream( out ); BufferedWriter cout = new BufferedWriter( new OutputStreamWriter( cs ) ); cout.write( ... ) But got null pointer exception in line 3: java.lang.NullPointerException at org.apache.hadoop.io.compress.zlib.ZlibFactory.isNativeZlibLoaded(ZlibFactory.java:63) at org.apache.hadoop.io.compress.GzipCodec.createOutputStream(GzipCodec.java:92) at myFile$myReduce.reduce(myFile

I package a text file with my Android App (in Assets) which I read within the App itself. To avoid this file being compressed, it's named 'mytestfile.mp3' and until recently, that worked just fine. In one of the recent SDK/ADT changes, it seems something 'odd' is happening when reading from Assets and I'm open to ideas as to what it is... I use code something like this AssetFileDescriptor descriptor = getAssets().openFd("mytextfile.mp3"); BufferedReader f = new BufferedReader(new FileReader(descriptor.getFileDescriptor())); String line = f.readLine(); while (line != null) { // do stuff Log.d(

Is it possible to compress an image before saving it? I'm using the Robot class to capture images, and it returns a BufferedImage. How can I compress this image and then save it? .png files are (losslessly) compressed images. You can use ImageIO.write() to save a .png image : ImageIO.write(myBufferedImage, "png", outputfile); There is colour compression ("compression quality") and there is resolution compression ("resizing"). E.g. I got a 4mb photo to 270K using a very low "compression quality", but that looked awful, but I got it down to 12K using a reasonable quality but a smaller size. My

In a nutshell, I programmed myself into a corner by creating a CLR aggregate that performs row id concatenation, so I say: select SumKeys(id), name from SomeTable where name='multiple rows named this' and I get something like: SumKeys name -------- --------- 1,4,495 multiple rows named this But it dies when SumKeys gets > 8000 chars and I don't think I can do anything about it. As a quick fix (it's only failing 1% of the time for my application) I thought I might compress the string down and I thought some of you bright people out there might know a slick way to do this. Something like base64

I have "noise" image... what is the best way to compress this image ? It can be little lossy. Techniques based on DCT and Wavelet are bad for this sort of problem. My idea was to generate some recreatable noise and then store only differences... but I cant find any solution for recreatable noise images. Image example: Well, it's almost the definition of noise to not be compressible. This statement applies to "real noise", when there is no known correlation between the perceived output and another applicable rule. So if your image is just that, or built to look like it is, then sorry, it's not

function uncompress($srcName, $dstName) { $sfp = gzopen($srcName, "rb"); $dstName = str_replace('.gz', '', $dstName); $fp = fopen($dstName, "w"); fseek($FileOpen, -4, SEEK_END); $buf = fread($FileOpen, 4); $GZFileSize = end(unpack("V", $buf)); while ($string = gzread($sfp, $GZFileSize)) { fwrite($fp, $string, strlen($string)); } gzclose($sfp); fclose($fp); } I use this code for uncompressing but It does not work and I get following error: Internal Server Error The server encountered an internal error or misconfiguration and was unable to complete your request. Please contact the server

I created a Python script to compress text by using the Huffman algorithm. Say I have the following string: string = 'The quick brown fox jumps over the lazy dog' Running my algorithm returns the following 'bits': result = '01111100111010101111010011111010000000011000111000010111110111110010100110010011010100101111100011110001000110101100111101000010101101110110111000111010101110010111111110011000101101000110111000' By comparing the amount of bits of the result with the input string, the algorithm seems to work: >>> print len(result), len(string) * 8 194 344 But now comes the question: how do