Optimization of entropy encoding using random variations

2021 ◽  
Author(s):  
Andrea Berdondini
Keyword(s):  
Entropy Encoding

scholarly journals Huffman Encoding using VLSI

2012 ◽  
pp. 143-145
Author(s):  
Shilpa.K. Meshram ◽  
Meghana .A. Hasamnis
Keyword(s):  
Binary Tree ◽  
Input Data ◽  
Huffman Coding ◽  
Specific Method ◽  
Prefix Code ◽  
Huffman Encoding ◽  
Lossless Data Compression ◽  
Variable Length Coding ◽  
Entropy Encoding ◽  
Tree Method

Huffman coding is entropy encoding algorithm used for lossless data compression. It basically uses variable length coding which is done using binary tree method. In our implementation of Huffman encoder, more frequent input data is encoded with less number of binary bits than the data with less frequency.This way of coding is used in JPEG and MPEG for image compression. Huffman coding uses a specific method for choosing the representation for each symbol, resulting in a prefix code. Prefix-free codes means the bit string representing some particular symbol is never a prefix of the bit string representing any other symbol.

scholarly journals Analysis of Fractals, Image Compression, Entropy Encoding, Karhunen-Loève Transforms

2009 ◽  
Vol 108 (3) ◽  
pp. 489-508 ◽  
Author(s):  
Palle E. T. Jorgensen ◽  
Myung-Sin Song
Keyword(s):  
Image Compression ◽  
Entropy Encoding

Conditional entropy encoding of log-PCM speech

1985 ◽  
Vol 21 (11) ◽  
pp. 475-476
Author(s):  
H. Gharavi ◽  
R. Steele
Keyword(s):  
Conditional Entropy ◽  
Entropy Encoding

scholarly journals Novel Multiple Bypass Bin Scheme and Low-power Approach for HEVC CABAC Binary Arithmetic Encoder

2018 ◽  
Vol 13 (3) ◽  
pp. 1-11
Author(s):  
Fábio Luís Livi Ramos ◽  
Bruno Zatt ◽  
Marcelo Schiavon Porto ◽  
Sergio Bampi
Keyword(s):  
Low Power ◽  
Video Processing ◽  
Input Data ◽  
Arithmetic Coding ◽  
Video Sequences ◽  
Critical Part ◽  
Binary Arithmetic ◽  
Coding Standards ◽  
Entropy Encoding ◽  
The Cost

HEVC is one of the most recent video coding standards, designed to face a new age of video processing challenges, such as higher video resolutions and limited traffic share bandwidth. The HEVC standard is divided into multiple steps, whereas the entropy encoding is the final stage before the coded bitstream generation. The CABAC (Context Adaptive Binary Arithmetic Coding) is the sole algorithm used for the entropy encoding at HEVC, providing reduced final bitstream generation, at the cost of increasing computational complexity and difficulties for parallelism opportunities. One of the novelties of the CABAC for the HEVC is the increase of certain types of input data (called bins), which have smaller dependencies among them (i.e. bypass bins), thus leading to the possibility to process multiples of them in parallel at once. The present work introduces a novel scheme for multiple bypass bins processing at once, leading to increasing bins-per-cycle throughput compared to related works. Moreover, the new technique is suitable for achieving a BAE (Binary Arithmetic Encoder) architecture (the CABAC critical part) able to process 8K UHD videos. Along with the multiple bypass bins technique, a low-power approach is achieved, based on statistical analysis of the recommended test video sequences, accomplishing around 15%of power savings.

scholarly journals Non-fixed Quantization Considering Entropy Encoding in HEVC

2011 ◽  
Vol 16 (6) ◽  
pp. 1036-1046 ◽  
Author(s):  
Ryeong-Hee Gweon ◽  
Woo-Jin Han ◽  
Yung-Lyul Lee
Keyword(s):  
Entropy Encoding

scholarly journals Optimization of entropy encoding using random variations

2021 ◽  
Author(s):  
Andrea Berdondini
Keyword(s):  
Probability Density ◽  
Average Length ◽  
Random Variables ◽  
Optimization Method ◽  
Uniform Probability ◽  
The Difference ◽  
Entropy Encoding ◽  
Average Information ◽  
Independent And Identically Distributed

ABSTRACT: This article describes an optimization method concerning entropy encoding applicable to a source of independent and identically-distributed random variables. The algorithm can be explained with the following example: let us take a source of i.i.d. random variables X with uniform probability density and cardinality 10. With this source, we generate messages of length 1000 which will be encoded in base 10. We call XG the set containing all messages that can be generated from the source. According to Shannon's first theorem, if the average entropy of X, calculated on the set XG, is H(X)≈0.9980, the average length of the encoded messages will be 1000* H(X)=998. Now, we increase the length of the message by one and calculate the average entropy concerning the 10% of the sequences of length 1001 having less entropy. We call this set XG10. The average entropy of X10, calculated on the XG10 set, is H(X10)≈0.9964, consequently, the average length of the encoded messages will be 1001* H(X10)=997.4 . Now, we make the difference between the average length of the encoded sequences belonging to the two sets ( XG and XG10) 998.0-997.4 = 0.6. Therefore, if we use the XG10 set, we reduce the average length of the encoded message by 0.6 values ​​in base ten. Consequently, the average information per symbol becomes 997.4/1000=0.9974, which turns out to be less than the average entropy of X H(X)≈0.998. We can use the XG10 set instead of the X10 set, because we can create a biunivocal correspondence between all the possible sequences generated by our source and ten percent of the sequences with less entropy of the messages having length 1001. In this article, we will show that this transformation can be performed by applying random variations on the sequences generated by the source.

Sign in / Sign up

Export Citation Format

Share Document