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January 18, 2016

Fast YUV to RGB conversion in Python 3

This Python scripts implements fast matrix-based conversion from YUV to RGB colospace. Conversion from YV12 (YUV420) requires one extra line of code to perform up-sampling of chroma planes.

July 12, 2015

A Python script evaluating PSNR metric of two YUV420 frames

A script evaluating PSNR metric of two YUV420 frames.
usage: filename1.yuv filename2.yuv frame_width frame_height
Alternatively if filename1 contains width and height in the form file-1200x1600.yuv,
the script will extract width and height of a frame from the filename. Then usage even simplier: filename1-1200x1600.yuv filename2.yuv

November 17, 2014

The maturity of H.265/HEVC video compression on the study of X265 v.1.4

In this post I share my test results on x265 coding efficiency to track the development progress. The x265 version 1.4 was used for this test, as well as the JCT-VC test sequences. It is worth mentioning that the Class E sequence set has changed, as described in JCTVC-O0022. However, for backward compatibility of the test results I still use the old test sequences set.

July 23, 2014

Xiph's Daala Compression Efficiency Update

Nine months have passed since my last comparisson of the HEVC, VP9 and Daala compression efficiency. Daala was in the development stage and is still being developed. Recently I've updated the codec to see the changes.
My apologies to the haters of PSNR and the lovers of SSIM. I'm still using PSNR.

December 9, 2013

Intra Compression Efficiency in VP9 and HEVC

In this paper we get into detailed overview of intra compression data-flow in HEVC and VP9. We describe common and unique stages of both standards. Then we carry out experiments with JCT-VC HM and WebM VP9 encoders on intra compression efficiency.
We also turn some of the HEVC features off to get its dataflow as close to VP9 as possible. Finally we get into discussion of the efficiency of both codecs, the corresponding standards and their intra compression algorithms.

November 16, 2013


This post is a useful link to Tampere Image Database (TID) and visual quality metric test suite, that I would like to have close at hand.
TID is intended for evaluation of full-reference image visual quality assessment metrics. TID allows estimating how a given metric corresponds to mean human perception. For example, in accordance with TID2008, Spearman correlation between the metric PSNR (Peak Signal to Noise Ratio) and mean human perception (MOS, Mean Opinion Score) is 0.525.