國立虎尾科技大學 |

Perceptual Image Coding with Discrete Cosine Transform

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Perceptual Image Coding with Discrete Cosine Transform/ by Ee-Leng Tan, Woon-Seng Gan.
Author:	Tan, Ee-Leng.
other author:	Gan, Woon-Seng.
Description:	VIII, 69 p. 35 illus., 6 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Signal processing. -
Online resource:	https://doi.org/10.1007/978-981-287-543-3
ISBN:	9789812875433

Perceptual Image Coding with Discrete Cosine Transform
Tan, Ee-Leng.

Perceptual Image Coding with Discrete Cosine Transform[electronic resource] /by Ee-Leng Tan, Woon-Seng Gan. - 1st ed. 2015. - VIII, 69 p. 35 illus., 6 illus. in color.online resource. - SpringerBriefs in Signal Processing,2196-4076. - SpringerBriefs in Signal Processing,.

Introduction -- Computational Models for Just-Noticeable-Differences -- Perceptual Image Coding with Discrete Cosine Transform -- Validation of Computational Model for JND -- Concluding Remarks.

This book first introduces classic as well as recent computational models for just-noticeable-difference (JND) applications. Since the discrete cosine transform (DCT) is applied in many image and video standards (JPEG, MPEG-1/2/4, H.261/3), the book also includes a comprehensive survey of computational models for JND that are based on DCT. The visual factors used in these computational models are reviewed in detail. Further, an extensive comparative analysis of these models using quantitative and qualitative performance criteria is presented, which compares the noise shaping performance of these models with subjective evaluation and the accuracy between the estimated JND thresholds and subjective evaluation. There are many surveys available on computational models for JND; however, these surveys seldom compare the performance of computational models that are based on DCT. The authors’ survey of the computational models and their in-depth review of the visual factors used in them will help readers understand perceptual image coding based on DCT. The book also provides a comparative analysis of several perceptual image coders that are based on DCT, which are compatible with the highly popular and widely adopted JPEG standard.

ISBN: 9789812875433

Standard No.: 10.1007/978-981-287-543-3doiSubjects--Topical Terms:

561459
Signal processing.

LC Class. No.: TK5102.9

Dewey Class. No.: 621.382

Perceptual Image Coding with Discrete Cosine Transform
LDR:02845nam a22004215i 4500 001 970206
003 DE-He213
005 20200705215025.0
007 cr nn 008mamaa
008 201211s2015 si | s |||| 0|eng d
020 $a 9789812875433 $9 978-981-287-543-3
024 7 $a 10.1007/978-981-287-543-3 $2 doi
035 $a 978-981-287-543-3
050 4 $a TK5102.9
050 4 $a TA1637-1638
072 7 $a TTBM $2 bicssc
072 7 $a TEC008000 $2 bisacsh
072 7 $a TTBM $2 thema
072 7 $a UYS $2 thema
082 0 4 $a 621.382 $2 23
100 1 $a Tan, Ee-Leng. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1265793
245 1 0 $a Perceptual Image Coding with Discrete Cosine Transform $h [electronic resource] / $c by Ee-Leng Tan, Woon-Seng Gan.
250 $a 1st ed. 2015.
264 1 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2015.
300 $a VIII, 69 p. 35 illus., 6 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a SpringerBriefs in Signal Processing, $x 2196-4076
505 0 $a Introduction -- Computational Models for Just-Noticeable-Differences -- Perceptual Image Coding with Discrete Cosine Transform -- Validation of Computational Model for JND -- Concluding Remarks.
520 $a This book first introduces classic as well as recent computational models for just-noticeable-difference (JND) applications. Since the discrete cosine transform (DCT) is applied in many image and video standards (JPEG, MPEG-1/2/4, H.261/3), the book also includes a comprehensive survey of computational models for JND that are based on DCT. The visual factors used in these computational models are reviewed in detail. Further, an extensive comparative analysis of these models using quantitative and qualitative performance criteria is presented, which compares the noise shaping performance of these models with subjective evaluation and the accuracy between the estimated JND thresholds and subjective evaluation. There are many surveys available on computational models for JND; however, these surveys seldom compare the performance of computational models that are based on DCT. The authors’ survey of the computational models and their in-depth review of the visual factors used in them will help readers understand perceptual image coding based on DCT. The book also provides a comparative analysis of several perceptual image coders that are based on DCT, which are compatible with the highly popular and widely adopted JPEG standard.
650 0 $a Signal processing. $3 561459
650 0 $a Image processing. $3 557495
650 0 $a Speech processing systems. $3 564428
650 0 $a Optical data processing. $3 639187
650 0 $a Computer science—Mathematics. $3 1253519
650 1 4 $a Signal, Image and Speech Processing. $3 670837
650 2 4 $a Image Processing and Computer Vision. $3 670819
650 2 4 $a Discrete Mathematics in Computer Science. $3 670123
700 1 $a Gan, Woon-Seng. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 771755
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9789812875440
776 0 8 $i Printed edition: $z 9789812875426
830 0 $a SpringerBriefs in Signal Processing, $x 2196-4076 $3 1257405
856 4 0 $u https://doi.org/10.1007/978-981-287-543-3
912 $a ZDB-2-ENG
912 $a ZDB-2-SXE
950 $a Engineering (SpringerNature-11647)
950 $a Engineering (R0) (SpringerNature-43712)