Image Lossy Compression And The Discrete Consine Transform

Index

Introduction

Algorithm - Encoding

Example

Conclusions

JPEG: Image Lossy Compression and the Discrete
Consine Trnasform
Presented by:
Diego Vel´squez R´
a
ıos
diego.velasquez@ucsp.edu.pe
San Pablo Catholic University
Computer Science
Authors
Ken Cabeen 1
Peter Gent 2
1−2

College of the Redwoods - Department of Mathematics
Joint Photographic Experts GroupNovember 28, 2011
Diego Vel´squez R´
a
ıos

JPEG: Image Lossy Compression and the Discrete Consine Trna

Index

Introduction

Algorithm - Encoding

1

Algorithm - Encoding
Color space transformation
Downsampling
Discrete cosine transform
Quantization
Entropy coding
Decoding

3

Example

4

Conclusions

Introduction
Introduction
Goal

2

Example

ConclusionsDiego Vel´squez R´
a
ıos

JPEG: Image Lossy Compression and the Discrete Consine Trna

Index

Introduction

Algorithm - Encoding

Example

Conclusions

Introduction

Index
1

Introduction
Introduction
Goal

2

Algorithm - Encoding
Color space transformation
Downsampling
Discrete cosine transform
Quantization
Entropy coding
Decoding

3

Example

4Conclusions
Diego Vel´squez R´
a
ıos

JPEG: Image Lossy Compression and the Discrete Consine Trna

Index

Introduction

Algorithm - Encoding

Example

Conclusions

Introduction

JPEG - Joint Photographic Experts Group

The compression method is usually lossy, meaning that some
original image information is lost and cannot be restored,
possibly affecting image quality.

DiegoVel´squez R´
a
ıos

JPEG: Image Lossy Compression and the Discrete Consine Trna

Index

Introduction

Algorithm - Encoding

Example

Conclusions

Introduction

JPEG - Joint Photographic Experts Group

The compression method is usually lossy, meaning that some
original image information is lost and cannot be restored,
possibly affecting image quality.
The JPEG compression algorithmis based on two human
eye’s visual defects:

Diego Vel´squez R´
a
ıos

JPEG: Image Lossy Compression and the Discrete Consine Trna

Index

Introduction

Algorithm - Encoding

Example

Conclusions

Introduction

JPEG - Joint Photographic Experts Group

The compression method is usually lossy, meaning that some
original image information is lost and cannot be restored,possibly affecting image quality.
The JPEG compression algorithm is based on two human
eye’s visual defects:
It is much more sensitive to changes in luminance than
chrominance in.

Diego Vel´squez R´
a
ıos

JPEG: Image Lossy Compression and the Discrete Consine Trna

Index

Introduction

Algorithm - Encoding

Example

Conclusions

Introduction

JPEG - Joint PhotographicExperts Group

The compression method is usually lossy, meaning that some
original image information is lost and cannot be restored,
possibly affecting image quality.
The JPEG compression algorithm is based on two human
eye’s visual defects:
It is much more sensitive to changes in luminance than
chrominance in.
More easily notice small changes in brightness in homogeneous
areas.

DiegoVel´squez R´
a
ıos

JPEG: Image Lossy Compression and the Discrete Consine Trna

Index

Introduction

Algorithm - Encoding

Example

Conclusions

Goal

Index
1

Introduction
Introduction
Goal

2

Algorithm - Encoding
Color space transformation
Downsampling
Discrete cosine transform
Quantization
Entropy coding
Decoding

3

Example

4

Conclusions
Diego Vel´squezR´
a
ıos

JPEG: Image Lossy Compression and the Discrete Consine Trna

Index

Introduction

Algorithm - Encoding

Example

Conclusions

Goal

Main Goal

Reduce the size of the images to upload to the web

Diego Vel´squez R´
a
ıos

JPEG: Image Lossy Compression and the Discrete Consine Trna

Index

Introduction

Algorithm - Encoding

Example

Conclusions...