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