References [1] Ramanathan Palaniappan, Nitin Suresh and Nikil Jayant, “Objective measurement of transcoded video quality in mobile applications”,IEEE MoVID 2008 (workshop as a part of WoWMoM 2008), Newport Beach, CA, June 2008. Conclusion & Current Work • MTBF estimates from AVQ scores : – show a wide range across transcoding bit rates and codecs. – are subjectively more meaningful. – better represent the slight variations in degraded visual quality. • Our current focus (in a Cisco Research Project) : – the analysis of network artifacts (NA) on video quality – Using these automatic VQ measurements to enhance the streaming of IP video Today’s Video Delivery Scenario The Video Quality (VQ) Challenge • Develop subjectively meaningful metrics • Must be objective, enabling real time computation • Zero Reference (ZR) nature – independent of source video Our Solution • Mean Time Between failures (MTBF) – Failure refers to video artifacts deemed to be perceptually noticeable – Directly related to Mean Opinion Score (MOS) • Automatic Video Quality (AVQ) – Objective estimate of MTBF for the transcoding experiments Metrics used in our work • PSNR – Simple full Reference metric – Computes pixel by pixel difference per frame basis b/w source and processed video – Actual value not definitive but comparison b/w two values measures quality • Automatic Video Quality (AVQ) – Zero Reference metric – Based on Spatio temporal algorithms and knowledge of Human Visual System AVQ Metric • Computation based on output pixel values • AVQ score shows excellent subjective attributes Our Transcoding Platform – VLC • Flexible transcoding options like different codecs, variable bit rate, Experimental Setup Results Sample Frames Objective Measurement of Transcoded Video Quality in Mobile Applications Ramanathan Palaniappan, Nitin Suresh and Nikil Jayant School of ECE, Georgia Institute of Technology, Atlanta, GA 30332 Network Video Server & Transcoder Projectors 3G Smart Phones HD TVs Laptops LCD Displays MPEG 2 to H.264 Transcoder 384 – 192 Kbps MPEG 2 to MPEG 4 Transcoder 384 – 192 Kbps MPEG 2 Transrater 384 – 192 Kbps MPEG 2 512 Kbps 72 sec (QCIF) H.264 MPEG 4 MPEG 2 0 500 1000 1500 20 30 40 50 60 70 PSN R vs fram e num ber@ 192 Kbps Fram e num ber PSNR (dB) H.264 M PEG 4 M PEG 2 0 500 1000 1500 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 AVQ C A score vs fram e num ber@ 192 Kbps Fram e num ber AVQ C A score H .264 M PEG 4 M PEG 2 0 500 1000 1500 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 AVQ C A score vs fram e num berforM PEG 4 Fram e num ber AVQ C A score 384 Kbps 256 Kbps 192 Kbps 0 500 1000 1500 25 30 35 40 45 50 PSN R vs fram e num berforM PEG 4 Fram e num ber PSNR 384 Kbps 256 Kbps 192 Kbps Fig. 1 : (a) plots PSNR for the video transcoded into H.264, MPEG 4 and MPEG 2 at 192 Kbps. (b) plots AVQ compression artifact (CA) score for the same case (Range : 0 – best and 1 – worst). (a) (b) Fig. 2 : (a) plots PSNR for the same video transcoded at 192, 256 and 384 Kbps with the MPEG 4 transcoder. (b) plots AVQ compression artifact (CA) score for the same case. (a) (b) Bit rate 192 kbps 256 Kbps 384 Kbps Codec MTBF PSNR MTBF PSNR MTBF PSNR H.264 546 36.51 1406 37.31 10000 38.24 MPEG-4 7 34.37 22 35.65 1968 37.39 MPEG-2 5 33.90 13 34.88 140 36.78 Table 1 : PSNR (dB) & estimated MTBF (sec) based on AVQ CA Score (a) (c) (b) Fig. 3 : Frame # 508 transcoded at 192 Kbps into (a) MPEG 2, (b) MPEG 4 & (c) H.264 Fig. 4 : Frame # 1469 transcoded into MPEG 4 at (a) 192 Kbps, (b) 256 Kbps & (c) 384 Kbps (a) (b) (c)