Problem Statement

Problem Definition:-

--- To address the exisitng algorithms and limitations.
--- To address the compression/decompression issues in text, images, audio and video.
--- To address the redundancy isues as a result of compression/decompression.
--- To address the practical issues in web browsers as a case study.
--- To address the problems faced by websites affecting their web performance as a case study.
--- To address the performance problems faced during Online Gaming.

Problem Analysis:-

1. Addressing and Analyzing the existing compression algorithms and limitations
2. Addressing and Analyzing the compression/decompression issues in text, images, audio and video
(i) Analysis of problems faced in Text
(ii) Analysis of problems faced in Images
(iii) Analysis of problems faced in Audio
(iv) Analysis of problems faced in Video
3. Addressing and Analyzing the redundancy isues as a result of compression/decompression
(i) Spatial redundancy
(ii) Coding redundancy
(iii) Spectral redundancy
(iv) Psycho visual redundancy
(v) Temporal redundancy
4. Addressing and Analyzing the performance problems faced during Online Gaming

Problem Solutions:-

The text will be pre-rendered (especially fonts), pre-fetched, pre-filtered and then transmitted. Generally, text doesn't consist of samples (pixels). So, no issues. But, pre-rendering will be given weightage. The images will follow pixeling where the pixels are pre-fetched, pre-filtered, reconstructed sending to destination. The text in the image will be considered as sample, since font is stuck in image which becomes an image gradually. So, text can't be specially separated from images. For audios, the sampling will be done where the sound will be sampled (i.e. audio sampling) and then follows same mechanism as mentioned above.

On the basis of images: Usage of 'luminance matrix' (RGB format) before encoding tracking the whole color body and during decoding, it will produce the exact colors. This may not give us 100% satisfaction, but it will produce a standard quality. Whereas, for 'chrominance matrix', it follows (Cr, Cg, Cb). For every sample or pixel, it is denoted by Y, Cr and Cb, where Y is the luma (or luminance or luminance matrix) value and Cr, Cb are chrominance values. While using this mechanism for transmission, then the process is termed as the chrominance subsampling.
So, how the process works?
The chrominance pixel is placed over the image pixel and centroid is placed in the center of the chrominance pixel. The pixel or color alignment is done in the ratio of H:V:T, where H=Chrominance resolution horizontal, V=Chrominance resolution vertical and T=Chrominance resolution total. Depending upon the alignment, the subsampling pattern is done where it is being arranged accordingly for chrominance-sample (occupies center) and no-chrominance-sample (or luma-sample, which occupies the rest).

In text compression, there were supporting existing mechanism that follows lossless compression. Other than text, it follows lossy compression for images and videos. In videos, the frames were mostly affected and whereas in images, the colours and pixels are affected. Yet, there is no strong lossless supporting mechanisms for images and videos. So, "combining lossless and lossy gives us an hybrid solution that will turn out to be lossless".