TY - JOUR

T1 - Probability models for highly parallel image coding architecture

AU - Aulí-Llinàs, Francesc

AU - Bartrina-Rapesta, Joan

AU - Hernández-Cabronero, Miguel

N1 - Publisher Copyright:
© 2022 The Author(s)

PY - 2023/3

Y1 - 2023/3

N2 - A key aspect of image coding systems is the probability model employed to code the data. The more precise the probability estimates inferred by the model, the higher the coding efficiency achieved. In general, probability models adjust the estimates after coding every new symbol. The main difficulty to apply such a strategy to a highly parallel coding engine is that many symbols are coded simultaneously, so the probability adaptation requires a different approach. The strategy employed in previous works utilizes stationary estimates collected a priori from a training set. Its main drawback is that statistics are dependent of the image type, so different images require different training sets. This work introduces two probability models for a highly parallel architecture that, similarly to conventional systems, adapt probabilities while coding data. One of the proposed models estimates probabilities through a finite state machine, while the other employs the statistics of already coded symbols via a sliding window. Experimental results indicate that the latter approach improves the performance achieved by the other models, including that of JPEG2000 and High Throughput JPEG2000, at medium and high rates with only a slight increase in computational complexity.

AB - A key aspect of image coding systems is the probability model employed to code the data. The more precise the probability estimates inferred by the model, the higher the coding efficiency achieved. In general, probability models adjust the estimates after coding every new symbol. The main difficulty to apply such a strategy to a highly parallel coding engine is that many symbols are coded simultaneously, so the probability adaptation requires a different approach. The strategy employed in previous works utilizes stationary estimates collected a priori from a training set. Its main drawback is that statistics are dependent of the image type, so different images require different training sets. This work introduces two probability models for a highly parallel architecture that, similarly to conventional systems, adapt probabilities while coding data. One of the proposed models estimates probabilities through a finite state machine, while the other employs the statistics of already coded symbols via a sliding window. Experimental results indicate that the latter approach improves the performance achieved by the other models, including that of JPEG2000 and High Throughput JPEG2000, at medium and high rates with only a slight increase in computational complexity.

KW - Entropy coding

KW - Image coding

KW - Parallel computing

KW - Probability models

UR - http://www.scopus.com/inward/record.url?scp=85146242690&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/789d2c09-4612-3051-836f-08a507312c14/

U2 - 10.1016/j.image.2022.116914

DO - 10.1016/j.image.2022.116914

M3 - Article

SN - 0923-5965

VL - 112

JO - Signal Processing: Image Communication

JF - Signal Processing: Image Communication

M1 - 116914

ER -