GPU-Based Sample-Parallel Context Modeling for EBCOT in JPEG2000 Jiří Matela Vít Rusňák
Motivation JPEG2000 Context Modeling Parallel Context Modeling Results GPU-Based Sample-Parallel Context Modeling for EBCOT in JPEG2000 Jiří Matela [email protected] Vít Rusňák [email protected] Petr Holub [email protected] Faculty of Informatics MU Brno Institute of Computer Science MU Brno CESNET Prague MEMICS’10 1/50 Motivation JPEG2000 Context Modeling Parallel Context Modeling Results The Advanced Network Technologies Laboratory at FI • High-res low-latency video transmission 2/50 Motivation JPEG2000 Context Modeling Parallel Context Modeling Results Example of Applications of High-Res Video and Visualization Transmission Remote lecturing 3/50 Motivation JPEG2000 Context Modeling Parallel Context Modeling Results Example of Applications of High-Res Video and Visualization Transmission Remote collaborative environments 4/50 Motivation JPEG2000 Context Modeling Parallel Context Modeling Results Example of Applications of High-Res Video and Visualization Transmission Film postproduction 5/50 Motivation JPEG2000 Context Modeling Parallel Context Modeling Results High Resolution Visual Communication is Bandwidth Demanding • Uncompressed HD video ∼ 1.5 Gbps • Uncompressed 4K video ∼ 6.4 Gbps • Several streams might be transmitted • Network connections may not have enough of bandwidth • Need for compression 6/50 Motivation JPEG2000 Context Modeling Parallel Context Modeling Results High Resolution Visual Communication is Bandwidth Demanding • Uncompressed HD video ∼ 1.5 Gbps • Uncompressed 4K video ∼ 6.4 Gbps • Several streams might be transmitted • Network connections may not have enough of bandwidth • Need for compression 7/50 Motivation JPEG2000 Context Modeling Parallel Context Modeling Results High Resolution Visual Communication is Bandwidth Demanding • Uncompressed HD video ∼ 1.5 Gbps • Uncompressed 4K video ∼ 6.4 Gbps • Several streams might be transmitted • Network connections may not have enough of bandwidth • Need for compression 8/50 Motivation JPEG2000 Context Modeling Parallel Context Modeling Results High Resolution Visual Communication is Bandwidth Demanding • Uncompressed HD video ∼ 1.5 Gbps • Uncompressed 4K video ∼ 6.4 Gbps • Several streams might be transmitted • Network connections may not have enough of bandwidth • Need for compression 9/50 Motivation JPEG2000 Context Modeling Parallel Context Modeling Results High Resolution Visual Communication is Bandwidth Demanding • Uncompressed HD video ∼ 1.5 Gbps • Uncompressed 4K video ∼ 6.4 Gbps • Several streams might be transmitted • Network connections may not have enough of bandwidth • Need for compression 10/50 Motivation JPEG2000 Context Modeling Parallel Context Modeling Results JPEG2000 characteristics • Successor to the popular JPEG • Superior compression performance • Lossy and lossless compression • Error resilience 11/50 Motivation JPEG2000 Context Modeling Parallel Context Modeling Results Multiple resolution representation 12/50 Motivation JPEG2000 Context Modeling Parallel Context Modeling Results Progressive transmission by pixel and resolution accuracy 13/50 Motivation JPEG2000 Context Modeling Parallel Context Modeling Results JPEG2000 compression process Data compression DWT Context Modeling Arithmetic Encoding 14/50 Motivation JPEG2000 Context Modeling Parallel Context Modeling Results Discrete Wavelet Transform (DWT) • Digital signal processing technique • Application in diverse areas • digital speech recognition • multi-resolution video processing • data compression 15/50 Motivation JPEG2000 Context Modeling Parallel Context Modeling Results DWT – Speedup Our GPU implementation shows 68× speedup 16/50 Motivation JPEG2000 Context Modeling Parallel Context Modeling Results Context Modeling (CM) in JPEG2000 • Analyzes and describes image to the Arithmetic Encoder • Described in form of CX,D pairs 17/50 Motivation JPEG2000 Context Modeling Parallel Context Modeling Results CM: Bit-Plane by Bit-Plane Source: wikipedia.org 18/50 Motivation JPEG2000 Context Modeling Parallel Context Modeling Results CM: Bit-Plane Scanned According to Scan Pattern 19/50 Motivation JPEG2000 Context Modeling Parallel Context Modeling Results CM: Bit-Plane Scanned According to Scan Pattern 20/50 Motivation JPEG2000 Context Modeling Parallel Context Modeling Results CM: Bit-Plane Scanned According to Scan Pattern 21/50 Motivation JPEG2000 Context Modeling Parallel Context Modeling Results CM: Bit-Plane Scanned According to Scan Pattern 22/50 Motivation JPEG2000 Context Modeling Parallel Context Modeling Results CM: Bit-Plane Scanned in 3 Passes 23/50 Motivation JPEG2000 Context Modeling Parallel Context Modeling Results CM: Bit-Plane Scanned in 3 Passes 24/50 Motivation JPEG2000 Context Modeling Parallel Context Modeling Results CM: Bit-Plane Scanned in 3 Passes 25/50 Motivation JPEG2000 Context Modeling Parallel Context Modeling Results CM: Coding Operations In a Pass • Zero Coding (ZC) • Run-Length Coding (RLC) • Magnitude Refinement Coding (MRC) • Sign Coding (SC) Coding operations produces CX,D pairs 26/50 Motivation JPEG2000 Context Modeling Parallel Context Modeling Results CM: Coding Operations In a Pass • Zero Coding (ZC) • Run-Length Coding (RLC) • Magnitude Refinement Coding (MRC) • Sign Coding (SC) Coding operations produces CX,D pairs 27/50 Motivation JPEG2000 Context Modeling Parallel Context Modeling Results CM: Coding Operations In a Pass • Zero Coding (ZC) • Run-Length Coding (RLC) • Magnitude Refinement Coding (MRC) • Sign Coding (SC) Coding operations produces CX,D pairs 28/50 Motivation JPEG2000 Context Modeling Parallel Context Modeling Results CM: Coding Operations In a Pass • Zero Coding (ZC) • Run-Length Coding (RLC) • Magnitude Refinement Coding (MRC) • Sign Coding (SC) Coding operations produces CX,D pairs 29/50 Motivation JPEG2000 Context Modeling Parallel Context Modeling Results CM: Coding Pass Decision Based on value, state, and states of neighbours 30/50 Motivation JPEG2000 Context Modeling Parallel Context Modeling Results CM: State variables Used: • for Coding Pass decision 31/50 Motivation JPEG2000 Context Modeling Parallel Context Modeling Results CM: State variables Used: • for Coding Pass decision • for Coding Operation decision 32/50 Motivation JPEG2000 Context Modeling Parallel Context Modeling Results CM: State variables Used: • for Coding Pass decision • for Coding Operation decision • by Coding Operations (to compute CX,D) 33/50 Motivation JPEG2000 Context Modeling Parallel Context Modeling Results CM: State variables Computed Step-by-Step (as bits are coded) 34/50 Motivation JPEG2000 Context Modeling Parallel Context Modeling Results CM: State variables Limits parallel processing 35/50 Motivation JPEG2000 Context Modeling Parallel Context Modeling Results CM: State variables Limits parallel processing Limits utilization of GPU 36/50 Motivation JPEG2000 Context Modeling Parallel Context Modeling Results Parallel Context Modeling Design • Replace the original states with 2 new states • Precomputed in parallel (before bit-plane is coded) • Bypass the defined coding passes • In-place coding operation decision • Bits coded in parallel 37/50 Motivation JPEG2000 Context Modeling Parallel Context Modeling Results Parallel Context Modeling Design • Replace the original states with 2 new states • Precomputed in parallel (before bit-plane is coded) • Bypass the defined coding passes • In-place coding operation decision • Bits coded in parallel 38/50 Motivation JPEG2000 Context Modeling Parallel Context Modeling Results Parallel Context Modeling Design • Replace the original states with 2 new states • Precomputed in parallel (before bit-plane is coded) • Bypass the defined coding passes • In-place coding operation decision • Bits coded in parallel 39/50 Motivation JPEG2000 Context Modeling Parallel Context Modeling Results Parallel Context Modeling Design • Replace the original states with 2 new states • Precomputed in parallel (before bit-plane is coded) • Bypass the defined coding passes • In-place coding operation decision • Bits coded in parallel 40/50 Motivation JPEG2000 Context Modeling Parallel Context Modeling Results Parallel Context Modeling Design • Replace the original states with 2 new states • Precomputed in parallel (before bit-plane is coded) • Bypass the defined coding passes • In-place coding operation decision • Bits coded in parallel 41/50 Motivation JPEG2000 Context Modeling Parallel Context Modeling Results Original State Variables • σ first non-zero bit of a pixel has been coded (pixel become significant) • σ 0 MRC has been applied • η a bit has been coded in SPP 42/50 Motivation JPEG2000 Context Modeling Parallel Context Modeling Results New State Variables • ρ pixel is already significant (become significant on previous bit-planes) • τ pixel going to be significant on current bitplane • Replaces the σ state 43/50 Motivation JPEG2000 Context Modeling Parallel Context Modeling Results Direct selection of coding operations Combination of τ and ρ replaces the original states 44/50 Motivation JPEG2000 Context Modeling Parallel Context Modeling Results Use of new variables for coding • MRC uses ρp+1 and τ p of all the neighbors instead of σ; the σ 0 is substituted by looking for the position of the first non-zero bit on previous p + 1 bit-planes. τ p of four preceding neighbors for bits belonging to SPP. ρp+1 and τ p all neighbors and bit value of the four preceding neighbors are used for bits belonging to CUP. • instead of σ, SC uses ρp+1 of two vertical and two horizontal neighbors and τ p of the upper and the left side neighbor for bits belonging to SPP. ρp+1 and τ p of two vertical and two horizontal neighbors and bit value of of the upper and the left side neighbor for bits belonging to CUP. • RLC uses no state information at all, both prior and after the transformation. 45/50 Motivation JPEG2000 Context Modeling Parallel Context Modeling Results Coding Speed Compared • OpenJPEG (cpu) • JasPer (cpu) • Kakadu (cpu) • CUJ2K (gpu) 46/50 Motivation JPEG2000 Context Modeling Parallel Context Modeling Results Speedup Speedup of the data compression part (Tier-1) Speedup OpenJPEG 5,3× JasPer 2,7× Kakadu 2,3× CUJ2K 1,9× 47/50 Motivation JPEG2000 Context Modeling Parallel Context Modeling Results Speedup Speedup of the Context Modeling Speedup OpenJPEG 26× JasPer 12× Kakadu N/A CUJ2K N/A 48/50 Motivation JPEG2000 Context Modeling Parallel Context Modeling Results Conclusion • Original algorithm reformulated • Approach to Context Modeling in JPEG2000 • Enables effective parallel implementation on GPU platforms • Preliminary results show 12× speedup • Acceleration of arithmetic encoding for JPEG2000 49/50 Motivation JPEG2000 Context Modeling Parallel Context Modeling Results Thank you for your attention! Q?/A! [email protected] 50/50
© Copyright 2024