In this paper, a rate-distortion optimized quantization scheme is described with application to H.264 video encoding. An efficient implementation of H.264 macroblock level adaptive quantization parameter selection is also described. Together these two encoder-only changes can achieve on average over 6% bit rate reduction under common testing conditions that are used in the H.264 standardization community. The described techniques provide this improvement in compression capability while retaining conformance of the encoded data to the H.264 standard. Thus, full compatibility with standard decoders can be achieved when applying these techniques.
In medical imaging, the popularity of image capture modalities such as multislice CT and MRI is resulting in an
exponential increase in the amount of volumetric data that needs to be archived and transmitted. At the same time, the
increased data is taxing the interpretation capabilities of radiologists. One of the workflow strategies recommended for
radiologists to overcome the data overload is the use of volumetric navigation. This allows the radiologist to seek a
series of oblique slices through the data. However, it might be inconvenient for a radiologist to wait until all the slices
are transferred from the PACS server to a client, such as a diagnostic workstation. To overcome this problem, we
propose a client-server architecture based on JPEG2000 and JPEG2000 Interactive Protocol (JPIP) for rendering
oblique slices through 3D volumetric data stored remotely at a server. The client uses the JPIP protocol for obtaining
JPEG2000 compressed data from the server on an as needed basis. In JPEG2000, the image pixels are wavelet-transformed
and the wavelet coefficients are grouped into precincts. Based on the positioning of the oblique slice,
compressed data from only certain precincts is needed to render the slice. The client communicates this information to
the server so that the server can transmit only relevant compressed data. We also discuss the use of caching on the client
side for further reduction in bandwidth requirements. Finally, we present simulation results to quantify the bandwidth
savings for rendering a series of oblique slices.
One of the key properties of the JPEG2000 standard is that it is possible to parse a JPEG2000 bit-stream to extract a lower resolution and/or quality image without having to perform dequantization and requantization. This property is especially useful given the variety of devices with vastly differing bandwidth and display capabilities that can now access the Internet. It is anticipated that a high-resolution JPEG2000-compressed image stored at an image server will be accessed by a variety of clients with differing needs for resolution and image quality. To satisfy the needs of these heterogeneous clients, it is essential that the server have the ability to transcode a JPEG2000 image in an efficient manner with very little loss in image quality. In this paper, we present a number of methods for transcoding a JPEG2000 image and evaluate each with respect to computational complexity and the quality of the transcoded image.
Ideally, when the same set of compression parameters are used, it is desirable for a compression algorithm to be idempotent to multiple cycles of compression and decompression. However, this condition is generally not satisfied for most images and compression settings of interest. Furthermore, if the image undergoes cropping before recompression, there is a severe degradation in image quality. In this paper we compare the multiple compression cycle performance of JPEG and JPEG2000. The performance is compared for different quantization tables (shaped or flat) and a variety of bit rates, with or without cropping. It is shown that in the absence of clipping errors, it is possible to derive conditions on the quantization tables under which the image is idempotent to repeated compression cycles. Simulation results show that when images have the same mean squared error (MSE) after the first compression cycle, there are situations in which the images compressed with JPEG2000 can degrade more rapidly compared to JPEG in subsequent compression cycles. Also, the multiple compression cycle performance of JPEG2000 depends on the specific choice of wavelet filters. Finally, we observe that in the presence of cropping, JPEG2000 is clearly superior to JPEG. Also, when it is anticipated that the images will be cropped between compression cycles when using JPEG2000, it is recommended that the canvas system be used.
Spatially varying quantization schemes try to exploit the non-stationary nature of image subbands. One technique for spatially varying quantization is classification based on AC energy of blocks. Several different methods of subband classification have been proposed in the literature. One method is to optimally classify each subband and send the classification maps as side information. Although image subbands can be shown to be roughly uncorrelated, they are not independent. Naveen and Woods proposed a method in which classification is done based on the AC energy of the block corresponding to the same spatial location, but from the lower frequency band. In their method, inter-subband dependence is exploited to almost completely eliminate side information, albeit at the cost of decreasing classification gain. In this paper, we proposed a new method of classification based on vector quantization of AC energy n-tuples formed by energies of blocks which correspond to the same spatial location in the original image but belong to different subbands. This method allows us to reduce the side information at the same time maximizing classification gain for each band under the vector constraint. The performance of the new method is compared with the other two methods. The comparison is made based on conditional entropies as well as actual bit rates.