1 / 26

FastVDO Unified 16-Bit Framework

FastVDO Unified 16-Bit Framework. Pankaj Topiwala FastVDO LLC Columbia, MD 21046 USA pnt@fastvdo.com JVT-B103. In the Beginning (April 01). April 01 – FastVDO showed how H.26L can be made fully 16-bit with no loss of performance (M16). At SB 4 other proposals also supported 16-bits

skimball
Download Presentation

FastVDO Unified 16-Bit Framework

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. FastVDO Unified 16-Bit Framework Pankaj Topiwala FastVDO LLC Columbia, MD 21046 USA pnt@fastvdo.com JVT-B103

  2. In the Beginning (April 01) • April 01 – FastVDO showed how H.26L can be made fully 16-bit with no loss of performance (M16). • At SB 4 other proposals also supported 16-bits • Key lessons learned • Negligible performance or complexity difference • Quantization is very flexible! TI/Sharp showed that it can be manipulated to make even the tml transform into 16-bits • Quant. memory rqmts can be minimized by periodicity • Proper focus – the transform – which can limit applications • Quantization can be safely decoupled

  3. Rapid Growth of Applications • But the application space of H.26L is growing: • low-rate wireless conversational (28 kb/s – 256 kb/s), • mid-rate streaming, VOD (64 kb/s – 1 Mb/s) • high-rate TV broadcasting (1- 4 Mb/s) • high-rate storage for future DVD (5-30 Mb/s) • Multirate digital cinema (mid-rate, visually lossless distribution, and lossless archive: 30 Mb/s, 200 Mb/s, and low Gb/s) • ultra-high rate HDTV (30 Mb/s – low Gb/s) • lossless medical (similar) • Entertainment applications poised to dominate.

  4. Desire • One framework that fits all applications • Reduce fragmentation of standard • Limit proliferation of inconsistent technologies • Improve interoperability between profiles • Significantly improve content reuse • FastVDO introduced such a framework • Still inadequately understood • Will now explain concretely

  5. Motivation • Similar coding performance as the DCT • Supporting a 16-bit (or less) architecture • Low complexity, multiplierless implementation (adds, right shifts) • Invertible integer-to-integer mapping • In-place computation

  6. DCT • Coding gain: 7.57 dB; Complexity: 8 adds, 6 mults in floating point • Integer approximation:

  7. Lifting Structure

  8. Generic 4-Pt Lifting Transform e f b c d Note: If a – u are dyadic rationals, then this - is exactly invertible! - has a mult-free def, multiple ways, and - is very stingy in bit expansion!

  9. Generic 4-Pt Inverse Transform - -

  10. e f b c d Example 1: FastVDO X1 - Hadamard a=u=1/2; b=c=d=e=f=p=1. Three equivalent implementations - matrix multiply - mult-free direct (8 adds, 0 scalings) - lifting (also mult-free: 8a 2s)

  11. e f b c d Example 2: FastVDO X2 a=1/2; b=c=d=e=f=1, u=p=-1. Three equivalent implementations - matrix multiply - mult-free direct (8 adds, 1 scalings) - lifting (also mult-free: 8a 1s)

  12. e f b c d Example 3: FastVDO X3 a=1/2; b=c=d=e=f=1, p=-2,u=2/5. W.K.Cham, 1989. X3 proposed by MS, Nokia. Non-dyadic numbers mean Non-invertible transform. Three equivalent implementations - matrix multiply - mult-free direct (8 adds, 2 scales) - lifting (but with mults!! – or approx. u)

  13. e f b c d Example 4: FastVDO X4 a=p=u=1/2; b=c=d=e=f=1. Note: High Coding Gain CG(X4) = 7.55 dB CG(DCT) = 7.57 dB Three equivalent implementations - matrix multiply - mult-free direct (9 adds, 2 scalings) - lifting (also mult-free: 8a 3s)

  14. e f b c d Example 5: FastVDO X5 a=1/2; b=c=d=e=f=1, p=7/16, u=3/8. Note: High Coding Gain CG(X5) = 7.57 dB CG(TML) = 7.57 dB CG(DCT) = 7.57 dB Three equivalent implementations - matrix multiply - mult-free direct (9 adds, 7 scales) - lifting (also mult-free: 10a 5s)

  15. x[0] Y[0] >>1 x[1] Y[2] x[2] Y[3] >>1 >>3 >>1 >>4 x[3] Y[1] Detailed Implementation of X5

  16. 1 2 3 4 Previous Integer DCT p 7/16 3/8 1/2 1/2 - - u 3/8 3/8 3/8 1/2 - - # adds 10 10 9 8 8 (16) 8 # shifts 5 5 4 3 0 (8) 0 # mults 0 0 0 0 6 (0) 6 CG in dB 7.57 7.56 7.55 7.55 7.57 7.57 Performance-Complexity

  17. Dynamic Range

  18. 8 x 8 BinDCT Coding gain: 8.77 to 8.82 dB for AR(1) process with p=0.95

  19. 16 x 16 BinDCT Coding gain: 9.4499 dB for AR(1) process with p=0.95

  20. Lessons Learned • All transforms considered fall under our rubric (other than tml) • No new transforms introduced in 9 months • Growing app. list needs transform innovation • Quantization is very flexible • Innovations have in fact been made in quantization • Sharp/TI showed that even tml can be made 16-bits • Quantization can be adapted to transform

  21. Lessons Learned (2) - TML • TML transform • OK for low-complexity, wireless app. using fixed hard-wired architectures that need matrix multiply • But unfriendly integers not good for ASIC • Not optimized for bit preservation, high-rate apps. • Not invertible • Not generalizable to larger transforms • Satisfies one transform method only – direct matrix multiply

  22. Lessons Learned (3) - Cham • OK for matrix and mult-free applications • Notionally adds 6 bits in forward transform • Needs truncation for higher-bit data • Likely penalty for high-rate, high-bit sources • Testing on high-bit data critical • Is not invertible (lifting not dyadic rational) • Does not generalize to higher transforms • Satisfies 2 (of 3) transform methods

  23. Relative Merits • General Comparisons • All contenders match current 32-bit performance • All offer reduced, nearly identical complexity • Unique Advantages • General framework to address broad range of needs • Very tight bit control • Demonstrated 16-bits output for 12-bit input, no truncation • Suitable for higher-bit data, and high rates • Related designs for higher sizes (8-pt, 16-pt) • Advantages of lifting improve further with size

  24. Currently No Concensus • First address the low-complexity, low-rate problem • Consider high-rate problem later, probably with a different transform • If lossless is needed, probably a 3rd transform • Energy misdirected to date • Some proponents backed single transforms, assumed original • Tests for performance, complexity metrics – inconclusive • Missing the bigger picture – support a wide variety of apps • Our vision – use a single framework if possible • Going forward – focus on our individual strengths

  25. Recommendations • Transform and Quantization can be decoupled • Adopt the framework • Prefer downloadable filters • Innovate in the transforms, goal of 3 transform methods • Finalize in the reflector, adopt in May • Tailor transform to wide variety of applications • Transform Activity can work directly in conjunction with other groups (e.g., Trans. Size, ABT, Interlace, Quantization, …) • Quant can focus on transform adaptation, finer quantization, periodicity, etc.

  26. Recommendations (2) • Focus transforms on high-quality, high-rate • Low-complexity case well understood • High-rate apps just emerging in JVT • Streaming, VOD • Broadcast (interlaced) • Film • Storage (DVD) • Higher block sizes • Review ABT options • Digital Cinema -- we have data • Look for synergies with low-complexity case

More Related