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

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 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

Method and system for wireless communication adapted for compressing data prior to its transmission. Data compression may be limited or skipped when it is determined that tlie data compression may cause an unacceptable amount or data to be lost. Abnormal situation detection as part of data compression is included. Methods associated with such systems are also encompassed.

Data compression and abnormal situation detection in a wireless sensor network

A video encoder includes a region detector module that classifies blocks of video frames. An adaptive filter module applies a median filter to a block based upon a block classification assigned by the region detector module. An adaptive quantization module quantizes a block according to a quantization method adaptively determined based upon a block classification assigned by the region detection module. In one example, a video encoder adaptively determines a median filter selected using a block classification. In another example, a video encoder adaptively determines whether to drop an isolated last transform coefficient based on the block classification, and/or applies a dead-zone selected using the block classification.

Adaptive video encoding using a perceptual model

The ability of the visual system to detect contrast in an image is a function of the frequency of the contrasting pattern and the distortion of the image. The visual system is more sensitive to contrasting patterns of lower frequency. When the image is significantly distorted, the visual system is even more sensitive to lower frequencies than higher frequencies. An image encoder employs lossy data compression processes producing a distorted reconstructed image. A method of quantizing image data including the step of varying the magnitude of a quantization step as a function of the distortion of an image is disclosed for further visually optimizing image quantization. Another method utilizes distortion adaptive weighting to vary the limit of code block truncation during embedded bitstream coding to visually optimize image compression by increasing relative lossiness of compression at higher frequencies.

Distortion-adaptive visual frequency weighting

PROBLEM TO BE SOLVED: To provide an image data compressing method utilizing a distortion adaptive frequency weighting method for image compression. SOLUTION: The capability of a visual system for detecting contrast in an image is a function of image distortion and a contrast pattern frequency. The visual system is more sensitive to a low frequency contrast pattern. When the image is distorted pretty much, the visual system is more sensitive to a low frequency than to a high frequency. An image encoder 26 adopts a data compressing process having a great number of losses in the case of forming a reconstructed image with distortion. An image data quantizing method including a step in which the magnitude of a quantizing step is changed as the function of image distortion is released in order to visually optimize image quantization. Another method changes the cutoff marginal value of a code block at the time of encoding an embedded bit stream by using distortion adaptive weighting and visually optimizes the compression of the image data by increasing a relative loss degree in compression in a high frequency.

Wenjun Zeng

Papers

Distortion-adaptive visual frequency weighting

Method for compressing image data, and method and device for quantizing data