Image Compression Based On BTC-DPCM And It ’ s Data-Driven Parallel Implementation Author ： Xiaoyan Yu 、 Iwata, M. Source ： Image Processing, ICIP IEEE International Conference on Speaker ： Cheng-Jung Wu Advisor ： Wen-Chien Chen

Outline Introduction Adaptive BTC on data-driven processing system Adaptive BTC algorithm Data-driven implementation Experimental evalution Conclusion

Introduction Image compression standards endure too heavy computational load in spite of good reconstructed quality with a very low bit rate The rate-distortion performance of the original BTC VQ 、 DCT AMBTC 、 ABTC Reconstructed quality and computational complexity ABTC algorithm coupled with DPCM

Adaptive BTC on data-driven processing system Most of the existing coding schemes do not care about their implementation in total An image compression algorithm and its implementation are considered as an integrated system ABTC Realize a fast coding on system-on-chip (SoC) Guarantee the reasonable image quality and compression ratio

Non-overlapping 4x4 pixel blocks Mean value ( ) Absolute moment ( AM ) Adaptive BTC algorithm

Each luminance block a uniform block a normal block a pattern block Decoder a uniform block reproduce the image a normal block a pattern block

DPCM algorithm Improve the bit rate with very small distortion of image quality DPCM neighboring pixels possess a high degree of correlation within an image

DPCM algorithm Three arbitral approaches two uniform blocks two consecutive normal blocks two adjacent pattern blocks

Data-driven implementation (a) illustrates a dataflow graph that sums up 16 input pixels of a block and calculates its mean by a 4 bit right-shift operation. In this case, an intermediate accumulated sum is fed back to the add operator repetitively The longest critical path influences the total pixel rate of the ABTC program.

Data-driven implementation (b) shows data-driven implementation by which the feedback path is distributively stuffed into each compound operator (read & add) so that the execution time of the critical path can be minimized at the software level Accepts a stream of 8 packets (i=1, …,8) each of which holds two neighbor pixels in a 4 x 4 block

Data-driven implementation Response time ( ) （ a ）： t  the time of the second pixel in a block image arrivingat add function （ b ）： t’  the time of the second pixel in a block image arriving at add function In case of DDMP

Experimental evalution Human is more sensitive to luminance changes rather than chrominance variances in an image. Thus, as for every chrominance block, the mean value is only calculated

Experimental evalution Visual quality of the proposed algorithm is competitive to that of JPEG2000 while its computational complexity is much less than that of JPEG2000

Experimental evalution The data-driven implementation of ABTC algorithm was performed using the variant number of processors on a single chip

Conclusion ABTC algorithm coupled with DPCM can achieve a better trade-off between reconstructed quality and computational complexity Both concurrent and pipelined parallelism inherent in the adaptive BTC were exploited and implemented on the DDMP chip