As I See Tech

While researching on compressed sparse matrix, I stumbled upon compressed row storage (CRS; compressed sparse row, CSR; Yale format) and compressed column storage (CCS; compressed sparse column, CSC). These sparse matrix compression formats are popular. Stepping back and imagining the possibilities, I considered the possibility of applying basic lossless compression techniques to sparse matrices, exploiting data redundancy, leading to what I call the compressed value storage (CVS) .

Compressed value storage (CVS) applies lossless compression to a matrix resulting to storage that can be smaller than the popular compressed matrix formats like CRS and CCS. In Visualizing Compressed Value Storage (CVS), I described an imagination of using CVS to store and render images. The overall idea is simple. In fact, it's so simple that it seems possible to implement the idea using JSON and HTML5 canvas.

This article describes an imagination of compressed value storage (CVS) being used in graphics storage and rendering. The imagination does not make any assumption that CVS can really be used for digital images. This is basically a spill of thought processes and no codes or implementations are shared. Still interested? Read on...