anchor.diff:  
   *3. Java Layout as 1D Pixel Array*
   
  -The most common approach is to copy the backing array behind Java's `BufferedImage` class and to reshape the memory using MATLAB (compare this link:https://mathworks.com/matlabcentral/answers/100155-how-can-i-convert-a-java-image-object-into-a-matlab-image-matrix#answer_109503[answer]).
  +The most common approach for getting image data is to copy the backing array of Java's `BufferedImage` class and to reshape the memory using MATLAB (compare this link:https://mathworks.com/matlabcentral/answers/100155-how-can-i-convert-a-java-image-object-into-a-matlab-image-matrix#answer_109503[answer]).
   
   [source,matlab]
   ----
  
positive.code: 
  This approach is the same as 3. with the difference being that the changes to the memory layout happen prior to copying into MATLAB.
  
  [source,matlab]
  ----
  % (1) Get data from byte[] and cast to correct type
  
negative.code: 
  *2. Compressed 1D Array*
  
  A common approach to move image data across distributed components (e.g. link:http://www.ros.org/[ROS]) is to encode the individual images using link:https://en.wikipedia.org/wiki/Motion_JPEG[MJPEG] compression before sending them. Doing this within a single process is obviously wasteful, but we included it because it is common practice in some systems. Since MATLAB does not offer a way to decompress jpeg images in memory, we need to save to data to file first.
  
  [source,matlab]
  ----
  