References [1] Ramanathan Palaniappan, Nitin Suresh and Nikil Jayant, “Objective

1. 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 • 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, GOP size etc. • 2 transcoding operations • MPEG 2 to H.264 • MPEG 2 to MPEG 4 SP Experimental Setup Results Sample Frames MPEG 2 to H.264 Transcoder 384 – 192 Kbps H.264 MPEG 2 512 Kbps 72 sec (QCIF) (a) (b) (c) MPEG 2 to MPEG 4 Transcoder 384 – 192 Kbps Fig. 3 : Frame # 508 transcoded at 192 Kbps into (a) MPEG 2, (b) MPEG 4 & (c) H.264 MPEG 4 MPEG 2 Transrater 384 – 192 Kbps (a) (b) (c) MPEG 2 Fig. 4 : Frame # 1469 transcoded into MPEG 4at (a) 192 Kbps, (b) 256 Kbps& (c) 384 Kbps • 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 (a) (b) 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). LCD Displays Laptops (a) (b) Network 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. Video Server & Transcoder 3G Smart Phones 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. HD TVs Projectors Table 1 : PSNR (dB) & estimated MTBF (sec) based on AVQ CA Score