Techniques and tools for encoding enhancement layer video with quantization that varies spatially and/or between color channels are presented, along with corresponding decoding techniques and tools. For example, an encoding tool determines whether quantization varies spatially over a picture, and the tool also determines whether quantization varies between color channels in the picture. The tool signals quantization parameters for macroblocks in the picture in an encoded bit stream. In some implementations, to signal the quantization parameters, the tool predicts the quantization parameters, and the quantization parameters are signaled with reference to the predicted quantization parameters. A decoding tool receives the encoded bit stream, predicts the quantization parameters, and uses the signaled information to determine the quantization parameters for the macroblocks of the enhancement layer video. The decoding tool performs inverse quantization that can vary spatially and/or between color channels.

Adaptive quantization for enhancement layer video coding

The disclosure is directed to techniques for region-of-interest (ROI) processing for video telephony (VT) applications. According to the disclosed techniques, a recipient device defines ROI information for video information transmitted by a sender device, i.e., far-end video information. The recipient device transmits the ROI information to the sender device. Using the ROI information transmitted by the recipient device, the sender device applies preferential encoding to an ROI within a video scene. In this manner, the recipient device is able to remotely control ROI encoding of far-end video information by the sender device.

Region-of-interest processing for video telephony

A video encoder identifies a current smooth region of a current picture in a sequence and performs temporal analysis by determining whether a corresponding region in at least one previous and/or future picture is smooth. Based at least in part on the temporal analysis, the encoder adjusts quantization in the current smooth region. An encoder determines a differential quantization interval for a sequence, the interval comprising an interval number. The interval constrains the encoder to skip differential quantization for at least the interval number of predicted pictures after a predicted differentially quantized picture. An encoder analyzes texture in a current picture and sets a smoothness threshold. The encoder compares texture data with the smoothness threshold and adjusts differential quantization for at least part of the current picture based on a finding of at least one smooth region in the current picture according to the smoothness threshold.

Quantization adjustment based on texture level

A sensing device includes: a video imager to obtain video of an area; a processing unit to receive and process the video from the video imager to identify one of an event or a datum of interest; and a communication channel to output a non-imagery signal representing the event or datum.

Intelligent imagery-based sensor

A video encoder identifies one or more AC coefficients of each of plural blocks in the picture. The encoder identifies a threshold quantization step size such that the identified AC coefficient(s) of each of the plural blocks are nonzero after quantization according to the threshold quantization step size. The threshold quantization step size is such that quantization according to the next higher quantization step size would result in at least one of the identified AC coefficient(s) of at least one of the plural blocks being zero. For example, identifying the threshold quantization step size comprises identifying n top AC coefficients in each of four blocks of a macroblock, determining the smallest AC coefficient among the identified n top AC coefficients of the four blocks, and iteratively evaluating the smallest AC coefficient with respect to candidate quantization step sizes until the threshold quantization step size is identified.

Adjusting quantization to preserve non-zero AC coefficients

We describe a method for dynamically variable region-of-interest editing of MPEG-2 video streams in the compressed domain to reduce bandwidth. This method is used in our second-generation mutually immersive mobile telepresence system, where only the region containing a user's head in an acquired video stream needs to be displayed at the remote location. This editing is performed in a way that leaves the resulting stream consistent with the MPEG-2 standard. We currently reduce the transmission of data by skipping macroblocks or deleting DCT coefficients outside the region of interest in P and B pictures, and deleting DCT coefficients in I frames. This results in a savings of approximately 50% of the bandwidth of the original stream for our application. Since we are transmitting four video streams to the surrogate over our wireless network, the total bandwidth is reduced by about 5 Mbit/s. As an added benefit, the CPU requirements for decoding the edited stream in software are also reduced by approximately 16%.

Region of interest editing of MPEG-2 video streams in the compressed domain

Switching theoretic approach to image compression

A novel approach for the lossless compression of images is presented. After preprocessing, the image is split into bit planes which are then divided into smaller blocks. Mixed blocks are converted to Boolean switching functions and then subjected to minimization to arrive at a compact representation, leading to possible data compression. Blocks are then classified into distinct events based on the outcome of logic minimization. A Huffman code is constructed and the blocks are encoded suitably. This approach provides a framework to incorporate various possible switching theoretic techniques into the basic coding scheme proposed by us. This paper also shows that logic minimization can be used to handle blocks of larger size, than practically possible with Huffman coding, to yield significant compression on gray-level images. Our approach compares well with JPEG in terms of compression ratio.

/pdf/lossless-compression-of-images-using-logic-minimization-4ex5nrivyl.pdf

Lossless Compression Of Images Using Logic Minimization

Lossless compression of images is important in fields like medical imaging and remote sensing. There are only a small number of good algorithms known to date for lossless image compression. We present a lossless image compression scheme which uses the concepts of logic coding and auto-adaptive block coding to obtain a scheme which performs comparable to the JPEG standard. The scheme reaches the best possible value of compression automatically, unlike the case of JPEG which gives a range of compression, based on available predictors.

/pdf/lossless-compression-of-images-using-minterm-coding-i2i7aboeve.pdf

Lossless compression of images using minterm coding

We propose a novel approach for the lossless compression of binary images using logic minimization. The image is divided into windows or blocks of size r/spl times/c pixels and each block is transformed into a Boolean switching function in cubical form, treating the pixel values as output of the function. Compression is performed by minimizing these switching functions using ESPRESSO, a cube-based two-level logic minimizer. To reduce the bits required to encode the minimized cubes (product terms), a code set which satisfies the prefix property is used. If this technique fails to produce compression for a window, the pixels are stored as such. The main motivation of the work has been to investigate the potential of logic minimization as a tool for image data compression. Our technique outperforms UNIX compress in terms of compression ratio on most of the test images. The compression scheme is relatively slower while the decompression time is comparable to that of UNIX compress.

/pdf/logic-minimization-based-approach-for-compressing-image-data-29g2q80cac.pdf

J. Augustine

Papers

Region of interest editing of MPEG-2 video streams in the compressed domain

Switching theoretic approach to image compression

Lossless Compression Of Images Using Logic Minimization

Lossless compression of images using minterm coding

Logic minimization based approach for compressing image data