31-05-2012, 04:28 PM
JPEG image compression:
JPEG image compression.doc (Size: 347.5 KB / Downloads: 72)
Introduction to JPEG image compression:
Today we are talking about digital networks, digital representation of images, movies, video, TV, voice, digital library-all because digital representation of the signal is more robust than the analog counterpart for processing, manipulation, storage, recovery, and transmission over long distances, even across the globe through communication networks. In recent years, there have been significant advancements in processing of still image, video, graphics, speech, and audio signals through digital computers in order to accomplish different application challenges. As a result, multimedia information comprising image, video, audio, speech, text, and other data types has the potential to become just another data type. Still image and video data comprise a significant portion of the multimedia data and they occupy the lion’s share of the communication bandwidth for multimedia communication. As a result, development of efficient image compression techniques continues to be an important challenge to us, both in academia and in industry.
Why Compression:
Despite the many advantages of digital representation of signals compared to the analog counterpart, they need a very large number of bits for storage and transmission. For example, a high-quality audio signal requires approximately 1.5 megabits per second for digital representation and storage. A television-quality low-resolution color video of 30 frames per second with each frame containing 640 x 480 pixels (24 bits per color pixel) needs more than 210 megabits per second of storage. As a result, a digitized one-hour color movie would require approximately 95 gigabytes of storage. The storage requirement for upcoming high-definition television (HDTV) of resolution 1280 x 720 at 60 frames per second is far greater. A digitized one-hour color movie of HDTV-quality video will require approximately 560 gigabytes of storage. A digitized 14 x 17 square inch radiograph scanned at 70 pm occupies nearly 45 megabytes of storage. Transmission of these digital signals through limited bandwidth communication channels is even a greater challenge and sometimes impossible in its raw form. Although the cost of storage has decreased drastically over the past decade due to significant advancement in microelectronics and storage technology, the requirement of data storage and data processing applications is growing explosively to outpace this achievement.
JPEG Compression:
After each input 8x8 block of pixels is transformed to frequency space using the DCT, the resulting block contains a single DC component, and 63 AC components. The DC component is predictive encoded through a difference between the current DC value and the previous. This mode only uses Huffman coding models, not arithmetic coding models which are used in JPEG extensions. This mode is the most basic, but still has a wide acceptance for its high compression ratios, which can fit many general applications very well.
Loss less Mode
Quite simply, this mode of JPEG experiences no loss when comparing the source image, to the reproduced image. This method does not use the discrete cosine transform, rather it uses predictive, differential coding. As it is loss less, it also rules out the use of quantization. This method does not achieve high compression ratios, but some applications do require extremely precise image reproduction.
Base Line Jpeg Compression
The baseline JPEG compression algorithm is the most basic form of sequential DCT based compression. By using transform coding, quantization, and entropy coding, at an 8-bit pixel resolution, a high-level of compression can be achieved. However, the compression ratio achieved is due to sacrifices made in quality. The baseline specification assumes that 8-bit pixels are the source image, but extensions can use higher pixel resolutions. JPEG assumes that each block of data input is 8x8 pixels, which are serially input in raster order.
Image Compression:
Image compression is an important topic in the digital world, whether it can be commercial photography, industrial imagery, or video. A digital image bitmap can contain considerably large amounts of data causing exceptional overhead in both computational complexity as well as data processing. Compression is important to manage large amounts of data for network, Internet, or storage media.
Conclusion:
The report presents a novel approach for image compression using the DCT transform. The magnitude and phase compression using this transformation have proved a good performance. Magnitude and phase were processed separately. The quantization of frequency samples in less bits has increased the compression ratio. Furthermore, the distributions used to generate the noise influence the result significantly. The lossy compression technique used seems not to degrade the image quality. A non-linear filter for smoothing the resulting image would be suitable for image enhancement. In general, the overall compression ratio is acceptable it compresses to about 15-30% the size of the original image. A lossless compression technique could be performed additionally to increase the compression factor. More levels of different bit length would probably improve the results. As future work, this compression method could be used for the complete DCT frequency spectrum instead of separately processing magnitude and phase.
JPEG image compression.doc (Size: 347.5 KB / Downloads: 72)
Introduction to JPEG image compression:
Today we are talking about digital networks, digital representation of images, movies, video, TV, voice, digital library-all because digital representation of the signal is more robust than the analog counterpart for processing, manipulation, storage, recovery, and transmission over long distances, even across the globe through communication networks. In recent years, there have been significant advancements in processing of still image, video, graphics, speech, and audio signals through digital computers in order to accomplish different application challenges. As a result, multimedia information comprising image, video, audio, speech, text, and other data types has the potential to become just another data type. Still image and video data comprise a significant portion of the multimedia data and they occupy the lion’s share of the communication bandwidth for multimedia communication. As a result, development of efficient image compression techniques continues to be an important challenge to us, both in academia and in industry.
Why Compression:
Despite the many advantages of digital representation of signals compared to the analog counterpart, they need a very large number of bits for storage and transmission. For example, a high-quality audio signal requires approximately 1.5 megabits per second for digital representation and storage. A television-quality low-resolution color video of 30 frames per second with each frame containing 640 x 480 pixels (24 bits per color pixel) needs more than 210 megabits per second of storage. As a result, a digitized one-hour color movie would require approximately 95 gigabytes of storage. The storage requirement for upcoming high-definition television (HDTV) of resolution 1280 x 720 at 60 frames per second is far greater. A digitized one-hour color movie of HDTV-quality video will require approximately 560 gigabytes of storage. A digitized 14 x 17 square inch radiograph scanned at 70 pm occupies nearly 45 megabytes of storage. Transmission of these digital signals through limited bandwidth communication channels is even a greater challenge and sometimes impossible in its raw form. Although the cost of storage has decreased drastically over the past decade due to significant advancement in microelectronics and storage technology, the requirement of data storage and data processing applications is growing explosively to outpace this achievement.
JPEG Compression:
After each input 8x8 block of pixels is transformed to frequency space using the DCT, the resulting block contains a single DC component, and 63 AC components. The DC component is predictive encoded through a difference between the current DC value and the previous. This mode only uses Huffman coding models, not arithmetic coding models which are used in JPEG extensions. This mode is the most basic, but still has a wide acceptance for its high compression ratios, which can fit many general applications very well.
Loss less Mode
Quite simply, this mode of JPEG experiences no loss when comparing the source image, to the reproduced image. This method does not use the discrete cosine transform, rather it uses predictive, differential coding. As it is loss less, it also rules out the use of quantization. This method does not achieve high compression ratios, but some applications do require extremely precise image reproduction.
Base Line Jpeg Compression
The baseline JPEG compression algorithm is the most basic form of sequential DCT based compression. By using transform coding, quantization, and entropy coding, at an 8-bit pixel resolution, a high-level of compression can be achieved. However, the compression ratio achieved is due to sacrifices made in quality. The baseline specification assumes that 8-bit pixels are the source image, but extensions can use higher pixel resolutions. JPEG assumes that each block of data input is 8x8 pixels, which are serially input in raster order.
Image Compression:
Image compression is an important topic in the digital world, whether it can be commercial photography, industrial imagery, or video. A digital image bitmap can contain considerably large amounts of data causing exceptional overhead in both computational complexity as well as data processing. Compression is important to manage large amounts of data for network, Internet, or storage media.
Conclusion:
The report presents a novel approach for image compression using the DCT transform. The magnitude and phase compression using this transformation have proved a good performance. Magnitude and phase were processed separately. The quantization of frequency samples in less bits has increased the compression ratio. Furthermore, the distributions used to generate the noise influence the result significantly. The lossy compression technique used seems not to degrade the image quality. A non-linear filter for smoothing the resulting image would be suitable for image enhancement. In general, the overall compression ratio is acceptable it compresses to about 15-30% the size of the original image. A lossless compression technique could be performed additionally to increase the compression factor. More levels of different bit length would probably improve the results. As future work, this compression method could be used for the complete DCT frequency spectrum instead of separately processing magnitude and phase.