An effective method for lossless video compression using intra-frame coding algorithms is described. Intra-frame coding is mainly used for still image compression and does not exploit a temporal correlation in video sequences. With the proposed method, multiple frames are combined into one large picture by interleaving all pixel data. In a large generated picture, the temporal correlation of the video sequence is transformed into a spatial correlation. A large picture enables images to be coded more efficiently and decreases the bitrate for lossless coding using intra-frame coding. We demonstrated the effectiveness of this method by encoding video sequences using JPEG 2000 and JPEG-LS.

In many applications of digital video database systems such as digital library, video data is often compressed with MPEG video algorithms. It will be an important technique to insert the additional information data like indexes and contents effectively into video database which is compressed with MPEG, because we can always deal with the additional information with video data itself easily. We propose a method for inserting optional binary data such as index information of digital library into MPEG-1 and -2 bitstreams. The binary data inserted MPEG video bitstreams using our proposed scheme are also according to the specification of the MPEG video frame structure. The proposed method allows us to extract the inserted binary data perfectly though MPEG-1 and -2 video are lossy algorithms. And the quality of decoded images after extracting added information is almost the same as that of ordinary MPEG bitstreams. Furthermore, traditional standard MPEG-1 and -2 video decoder which can not extract inserted binary data can also decode images from the binary data inserted MPEG video bitstreams without obvious image degradation. There are some different points between the proposed insertion technique of the binary data and the watermarking technique. The technique of watermarking prepares to deal with alter watermarking by others. And the technique of watermarking is required for the identification of the signature and the perfect extraction of the inserted image signature is not required in the lossy MPEG video environment. On the other hand, we have to extract all of the inserted binary information data correctly with the insertion technique of the binary information. Simulations using MPEG video sequences with inserted binary data are presented to quantify some performance factors concerned. We have not heard about inserting data method which purpose is such as index and content information insertion.

This paper describes an effective technique for coding QCIF video sequences based on a JPEG2000 codec. In the proposed method, multiple frames are combined into one large picture. The larger picture enables images to be coded more efficiently. Image quality is further improved by combining the frames symmetrically. The video sequence is efficiently coded by adapting the time correlation of the video sequences to spatial correlation. We demonstrated the effectiveness of this method by encoding QCIF video sequences using JPEG2000.

A method of scrambling MPEG video by exchanging the motion vector (MV) in the MPEG bitstream is proposed. It deals directly with the MPEG bitstream and exclusive MPEG encoders are unnecessary. The size of the scrambled bitstream does not increase and image quality is maintained after descrambling. Moreover, the structure of the MPEG bitstream is maintained and can be decoded with a standard MPEG video decoder. We demonstrate the effectiveness of this method through simulation results that reveal unchanged image quality and size of bitstreams.

In MPEG standard, motion estimation (ME) is used to eliminate the temporal redundancy of video frames. This ME is the most time-consuming task in the encoding of video sequences and is also the one using the most power. Using low-bit images can save power of ME and a conventional architecture fixed to a certain bit width is used for low-bit motion estimator. It is known that there is a trade-off between power and image quality. ME may be used in various situations, and the relation between demands for power or image quality will depend on those circumstances. We therefore developed an architecture for a low-bit motion estimator with adjustable power consumption. In this architecture, we can select the bit width for the input image and adjust the amount of power for ME. To evaluate its effectiveness, we designed the motion estimator by VHDL and used the synthesis results to estimate the performance.

This paper propose a method for improving the image quality of motion estimation (ME) using low-bit images. By using edge-enhanced images for quantization, we can increase the accuracy of the ME and improve the image quality. It is known that using low-bit images for ME is effective for reducing power consumption but it slightly degrades image quality. The quality of the encoded image depends on the thresholds for data quantization, thus, algorithms for determining thresholds are studied. The proposed method uses linear quantization, which simply truncates the least significant bits. This method is simple without any complicated threshold calculations, and the resultant image quality is improved as much as the methods that use threshold calculations. To evaluate the effectiveness, we simulate results for image quality and estimate the power consumption using synthesis results from a VHDL model motion estimator.